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      ← Back to Home
    


      

        
      
      

        
Week 14
        
Final Project
      
      
    


    

      

        
        Week Highlights
      
      
Final project integration week focused on bringing together all subsystems into a cohesive MirrorAge system, completing documentation, and preparing for final presentation.
    


    

      

        
        Table of Contents
      
      

        

          
Project Overview
          

            
• Week Highlights
            
• Remaining Tasks
            
• System Integration Plan
          
        
        

          
Development
          

            
• Reflections & Learnings
          
        
        

          
Documentation
          

            
• Contributions
            
• Ethical AI Use
          
        
      
    


    

      

        
        Remaining Tasks (Snapshot)
      
      

        Final integration week focused on bringing all subsystems together, completing hardware fabrication, firmware integration, and documentation for the MirrorAge system presentation.
      


      

        

          
          Week 14 Day-by-Day System Integration Plan
        


        

          

            
            

              
Theme of the Week
              

                FINITIATIVE
              
              

                The act of bringing something to a conclusion or ending in a decisive manner. The ability to efficiently finish tasks and projects with a goal-oriented mindset.
              
            
          
        


        



          

            
Wednesday
            

              

                Morning
                

                  
Publish system integration plan on website
                  
Update weekly assignment sections on final project page (weeks 10-13)
                  
Link final project design files
                  
Update reflections and learnings
                  
Update picture of prism holder
                  
Add final pictures to Slack canvases
                  
Create schematic between boards (wired and wireless) — updated system diagram on PPT-like page
                  
Start CAD model of system
                  
Place final order
                
              
              

                Afternoon & Evening
                

                  Design:
                  

                    
Boards: Combine OLED screen/accelerometer with pulse oximeter board in a new board (maybe upgrade to small TFT as minor)
                    
Boards: Speaker board with realtime amplifier and TFT (on the load cell fixed board)
                    
Band: Mold design
                    
Cases: 3D print mountable cases for the boards
                  
                
                

                  
Integrate designs into final CAD
                  
Document design
                  
Finish CAD model of system
                
              
            
          


          

            
Thursday
            

              

                Fabricate
                

                  
Milling boards
                  
Print mold and cast band
                  
Print cases
                
              
              

                Solder
                

                  
Solder components on milled boards
                  
Connect boards with wired (and wireless connection codes)
                
              
              

                Finishing
                

                  
Mirror on screen (easy way with the film)
                
              
              

                Documentation
                

                  
Document fabrication
                
              
            
          


          

            
Friday
            

              

                Programming
                

                  
Board level codes
                  
Server level codes
                
              
              

                Documentation
                

                  
Document codes
                
              
              

                Completion
                

                  
Finish anything else
                
              
            
          


          

            
Saturday
            

              

                Programming & Testing
                

                  
Demo integrated system
                  
Test integrated system
                
              
              

                Documentation
                

                  
Document testing and evaluation
                  
Review and complete documentation (list of questions)
                  
Make the video by collaging documentation
                
              
            
          


          

            
Sunday
            

              
Fill up any gaps
              
Prepare demo on tensegrity table
              
Finalize 1-slide
              
Work on minors
              
If done, work on if there's time
            
          


          

            
Monday Morning
            

              
Transport demo on tensegrity table
              
Fill up any gaps
              
Work on minors
            
          
        


        

          
Specific Tasks to Complete This Week


            

              Major Tasks
              

                
CAD model of system
                
Speaker board with realtime amplifier
                
Combine screen/accelerometer with pulse oximeter board in a new board
                
Mold and cast band
                
Design and fabricate casing (print)
                
Schematic between boards (wired and wireless) — updated system diagram on PPT-like page
                
Serial connection between pulse oximeter and tiny blinking heart for BPM (BPM from IR, SpO2 from delta)
                
Combine multiple boards on the same WiFi (switching tabs is easy way, board hosting the webpage querying and update or Python-based server somewhere where everything posting data, Raspberry Pi)
                
Put+program everything together according to the above (with WiFi for now)
                
Mirror on screen (easy way with the film)
                
Document evaluations and costs (plus the rest of the list here: project presentation requirements)
                
Summary slide and one-minute video for documentation
                  

                    
Conception
                    
Construction
                    
Operation
                  
                
              
            


              Minor Tasks
              

                
Program microphone/speaker
                
Fix reaction time delay code
                
Program LoRa connection
                
Fix OLED plus WiFi issue
                
Upgrade to TFT (SPI is very straightforward, design board with either SPI or OLED connection)
                
Fix heart engraving to center
                
Engrave K9 glass mirror if it arrives
                
RD and IRD isolation slit (maybe wick and then cut)
                
Do the calibration curve for the load cell
                
Finish cardboard laser cutter origami big mirror frame
              
            


              If There's Time
              

                
Moving base of mirror
              
            


              Orders
              

                
Raspberry Pi Zero (or server host, do some research)
              
            


              Presentation
              

                
Aim for 2.5 minutes because Neil will ask questions
                
Generally no slides except for 1 open summary slide (have backup slides in case questions come up!)
              
            
          
        
      
    


    

      

        
        Reflections & Learnings
      
      
Reflections and key learnings from the final project integration week will be documented here.
    


    

      

        
        Contributions
      
      
Acknowledgments for contributions and collaborations during the final project week.
    


    

      

        
        Ethical AI Use
      
      
Documentation of AI-assisted tasks and tools used during the final project integration week.
    


  

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2. Added "Remaining Tasks (Snapshot)" section — includes the system integration plan as a subsection.

3. Embedded positive possum image — placed at the top of the plan under "Theme of the Week" with: - Image: finalproject/motivation/positive_possum.jpg - Caption and description - Link to the finitiative definition

4. Added system integration plan — includes: - Theme of the Week (FINITIATIVE) with the possum image - Daily schedule (Wednesday through Monday morning) - Specific tasks organized by: - Major tasks - Minor tasks - If there's time - Orders - Presentation guidelines

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Saleem A. Al Dajani
        
MIT CEE PhD @ AbuGoot and Gladyshev Labs 
 
        HTMAA 2025 · Making + AI for Aging & Longevity
      
      
    


      

        
Welcome
        
This is my home base for How to Make (Almost) Anything. I’m documenting weekly assignments, fabrication experiments, and a final project that bridges making with aging & health analytics.
        

          🛠️ Digital fabrication
          🪞 Intrinsic capacity
          📈 Sensing & ML
          🎛️ Embedded systems
        
        

          Start with Week 1 →
          Explore projects
        
      
      

        
Now
        

          

            Final project focus

            MirrorAge: an edge-AI mirror that fuses grip strength, facial expression, voice, motion, reaction time, and wearable accelerometry into an intrinsic capacity score.
          
          

            Focus this week

            Set up documentation workflow, safety training, shop access, and first toolchain tests (CAD → CAM → cut/print).
          
          

            Local time

            Loading…
          
        
      
    


  

    
Weekly Documentation
    
      
Week 00 · Introduction & design 
      Setup, parametric design
    
    
      
Week 01 · Principles & cutting
      Version control, laser/vinyl
    
    
      
Week 02 · Embedded programming
      Electronics basics
    
    
      
Week 03 · 3D scanning & printing
      Meshes, AI tools
    
    
      
Week 04 · Electronics design
      EDA, schematics
    
    
      
Week 05 · Electronics production
      Debugging, assembly
    
    
      
Week 06 · Computer-controlled machining
      CAM, milling
    
    
      
Week 07 · Input devices
      Sensors, embedded architectures
    
    
      
Week 08 · Output devices
      Actuators, system integration
    
    
      
Week 09 · Molding & casting
      Forming, resins
    
    
      
Week 10 · Mechanical & machine design
      Kits, midterm review
    
    
      
Week 11 · Networking & communications
      BLE, Wi-Fi, AR/VR/MR
    
    
      
Week 12 · Interface & application programming
      UI, applications
    
    
      
Week 13 · Wildcard & final orders 
      Why make (almost) anything, final orders
    
    
      
Week 14 · Final Project
      System integration, documentation, presentation
    
    
      
Final Project · MirrorAge Intrinsic Capacity Mirror
      Development, integration, presentation
    
  



  

    
Lab Links & Logistics
    

      

        Class site — MAS.863
      
      

        Home base — EDS 38‑501 (hours: Sun 1p–12a; Mon–Thu 9a–12a; Fri 9a–5p)
      
      

        Safety & training — machine checkouts, PPE, material inventory
      
      

        Workflow — design (CAD) → toolpaths (CAM) → fabricate → test → iterate → document
      
    
  



      

        
MirrorAge Intrinsic Capacity Mirror
        
Edge AI mirror that combines SenseCraft vision, ReactionAge latency testing, grip-force sensing, wearable streaming, and molded packaging into one self-contained assessment system.
        

          embedded
          edge AI
          sensor fusion
        
      
      

        
Toolchain Starter Kit
        
Minimal templates for week docs (Markdown → HTML), BOM tables, parametric CAD files, and reproducible build recipes.
        

          templates
          BOM
          parametric CAD
        
      
    


      

        
Scratchpad
        

          
Design doc: problem → constraints → experiments → results → risks → next steps.
          
Document failures first; post photos, tool settings, and source files weekly.
          
Target 1 new process/week (laser, PCB mill, composites, etc.).
        
      
      

        
Checklists
        

          
✔️ Git repo + web hosting set up
          
⬜ Shop safety & machine training
          
⬜ BOM tracker + suppliers
          
⬜ Sensor test harness (ECG/PPG)
        
      
    


    

      
Reading & Inspiration
      
Fabrication, sensing, and health tech; plus a few works-in-progress from my broader research.
      

        
HTMAA Archive — past projects & processes for reference.
        
OpenAI × Retro — lab/AI synergy inspiration.
        
Trackio — simple experiment tracking (WIP).
      
    


    

      
Contact
      

        sdajani [at] mit.edu · 
        Google Scholar
      
    


    

      
Bio
      

        I am a PhD student in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), with joint affiliations at Brigham and Women's Hospital (BWH, part of Mass General Brigham) and Beth Israel Deaconess Medical Center (BIDMC, part of Beth Israel Lahey Health) at Harvard Medical School (HMS), where I am a member of the Abudayyeh-Gootenberg Lab and the Gladyshev Lab. I am also affiliated with the Mesoscale Nuclear Materials (MNM) Group in the Short Lab within the Department of Nuclear Science and Engineering at MIT. My research integrates the mechanics of materials under extreme conditions with machine learning to advance health, aging, and longevity science.
      
    


    

      
Ethical AI Usage
      
Session transcripts where ChatGPT helped refine designs, code, and plans for this course.
      

        

          
Week 0
          
Initial setup and homepage development
          

            
Drafting email for registration approval — 09/07/25
            
HTMAA homepage structure & styling — 09/10/25
          
          

            
              📄 View Detailed Week 0 AI Usage →
            
          
        
        

          
Week 1
          
CAD/CAM, laser cutting, and vinyl cutting
          

            
Thick origami conversion assistance — 09/15/25
          
          

            
              📄 View Detailed Week 1 AI Usage →
            
          
        
      


      

        
AI-Assisted HTML Generation Script
        

          Created a Python script to automatically convert Cursor AI markdown transcripts to styled HTML files for better browser viewing.
        
        

          📄 View Script
          💾 Download Script
        
        

          Usage: python3 scripts/md_to_html_converter.py input.md output.html "Title" "Description"
        
      


      

        
AI-Assisted Prompt Generation
        

          Developed systematic prompts for consistent footer updates and content management across all pages in the HTMAA documentation workflow.
        
        

          📄 View Prompt Generation
          📄 View Transcript
          💾 Download Markdown
        
        

          Focus: Systematic content updates, footer standardization, and documentation workflow optimization
        
      


      

        
AI-Assisted Responsive Design Refactoring
        

          Created comprehensive prompts for making the entire web repository fully responsive on phones while maintaining desktop-friendly design. Applied mobile-first responsive design principles, responsive tables, flexible layouts, and touch-friendly interactions.
        
        

          📄 View Prompt
        
        

          Focus: Mobile-first responsive design, viewport optimization, responsive tables, flexible layouts, and touch-friendly UI
        
      


      

        
AI-Assisted Week Pages Documentation Update
        

          Systematic update of all week pages with helpful documentation links, creation of week7.html for input devices, and comprehensive resource integration across the entire course documentation structure.
        
        

          📄 View Full Transcript
          💾 Download Markdown
        
        

          Focus: Week 7 creation, helpful documentation links integration, course resource organization, and systematic page updates across all weeks
        
      
    


    

      

        

          
            
          
          This work is licensed under a
          
            Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
          
        
      
    


  

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      ← Back to Home
    


      

        
      
      

        
Final Project
        
MirrorAge Intrinsic Capacity Mirror · HTMAA 2025
      
      
    


    

      

        
        Project Highlights
      


      

        

          
Camera Works!
          

            
              
            
          
          

            XIAO ESP32S3 camera successfully captures and displays images on OLED screen using Floyd-Steinberg dithering
          
        
        

          
EdgeAI Works!
          

            
              
            
          
          

            Real-time emotion classification running on XIAO ESP32S3 using SenseCraft AI platform
          
        
        

          
Selfie Capture!
          

            
              
            
          
          

            Interactive selfie capture system with touch controls and real-time display
          
        
      


      

        
Vision Board
        

          
        
        

          Conceptual visualization of the multimodal intrinsic capacity assessment system integrating grip strength, voice analysis, facial recognition, video motion capture, reaction time measurement, and wearable accelerometer data.
        
      


      

        
Intrinsic Capacity (IC) Coverage by Digital Biomarkers
        

          

            

              

                
Domain
                
Grip
                
Voice
                
Face
                
Video
                
Reaction Time
                
Wearable
              
            
            

              

                
Locomotor
                
✅
                
🔸
                
🔸
                
✅
                
🔸
                
✅
              
              

                
Cognition
                
❌
                
✅
                
🔸
                
🔸
                
✅
                
🔸
              
              

                
Vitality
                
✅
                
✅
                
✅
                
✅
                
🔸
                
✅
              
              

                
Sensory
                
❌
                
✅
                
✅
                
✅
                
✅
                
🔸
              
              

                
Psychological
                
❌
                
✅
                
✅
                
✅
                
🔸
                
✅
              
            
          
        
        

          ✅ Strong coverage | 
          🔸 Partial/indirect | 
          ❌ Not covered | 
          View full table →
        
      


      

        
Multimodal IC Pipeline
        

          

            Inputs: Grip Strength, Voice, Face, Video, Reaction Time, Wearable Accelerometer
          
          

            Process: Feature extraction → Embeddings → Fusion layer → IC Score
          
          

            Output: Overall Intrinsic Capacity Score + Domain sub-scores
          
        
        

          View detailed pipeline →
        
      


      

        
Weekly System Development
        

          

            Weeks 0-1: Project foundation, cutting techniques for device components
          
          

            Weeks 2-4: Electronics foundation, embedded programming, PCB design
          
          

            Weeks 5-7: Input devices, sensors for grip strength, voice, face detection
          
          

            Weeks 8-10: Output devices, mechanical design, system integration
          
          

            Weeks 11-13: Communication, UI development, final integration
          
        
        

          View detailed weekly breakdown →
        
      


      

        
Project Presentation
        

          

            Summary Slide:
            
              Download midterm deck (Oct 31) →
            
            
              Includes system overview, IC coverage, fabrication snapshots, and remaining risk register.
            
          
          

            Preview Videos:
            
              Preview videos from ReactionAge, EdgeAI selfie capture, as well as FaceTTD model and accelerometer on OLED.
            
            
              Teaser clips: ReactionAge latency demo ·
              Selfie capture loop ·
              FaceTTD camera display ·
              Accelerometer on OLED
            
          
        
        

          View full presentation materials →
        
      


      

        
Final Project Spiral Development Model
        

          
        
        

          

            Development Approach: Following the spiral model methodology, this final project will iterate through multiple development cycles, each building upon previous work while addressing new requirements and risks.
          
          

            

              Cycle 1: Minimal viable project for class scope
            
            

              Cycle 2: Core functionality expansion
            
            

              Cycle 3: Advanced features and integration
            
            

              Cycle 4: Future research applications
            
          
        
        

          Learn more about the Spiral Model →
        
      


      

        Source: ChatGPT Discussion on Intrinsic Capacity Biomarkers
      




    

      

        
        Midterm Review Checklist
      
      

        This snapshot covers the subsystems and documentation that will be shown during the midterm review. Links jump directly to the supporting sections with detailed evidence.
      
      

        

          Featured Subsystems
          

            
Camera subsystem — Edge inference demos and selfie capture loop.
            
ReactionAge module — Latency measurement firmware and UX.
            
Grip strength rig — Load cell calibration and enclosure iterations.
            
Voice pipeline — Audio capture stack and VoiceAge model notes.
            
Integration readiness plan — Outstanding work items and risk mitigation.
          
        
        

          Tasks Completed Before Review
          

            
System diagram — refreshed block diagram with annotated sensing, fusion, and feedback flows packaged for the midterm deck.
            
Task backlog snapshot — consolidated hardware, firmware, data, UX, and validation checklists that show current status and risk owners.
            
Week-of schedule — detailed execution calendar covering evidence capture, documentation polish, dry-run, and buffer windows.
            
Instructor meeting hold — Thursday, Nov 12 at 10:00 AM ET reserved via the shared HTMAA midterm review sheet.
          
        
        

          Execution Schedule (Timeline Snapshot)
          
Condensed from the Week 8–13 development timeline: each sprint builds toward final integration, mirroring the gantt chart below.
          

            
Week 8 · Output Devices: figuring out wiring for real-time display states.
            
Week 9 · Molding & Casting: learn how to cast custom housings and refine structural components.
            
Week 10 · Mechanical Design: figure out ergonomic enclosure and calibration fixtures.
            
Week 11 · Networking: program BLE/Wi-Fi telemetry and wearable data fusion.
            
Week 12 · Interface/App: create mobile UI, cloud bridge, and IC scoring pipeline.
            
Week 13 · Final Integration: run validation passes, document results, and prep deployment.
          
        
        

          Instructor Review Logistics
          

            Calendar hold sent for Thursday, Nov 12 at 10:00 AM ET (38-501 conference room) per the shared HTMAA scheduling sheet. Agenda covers subsystem demos, weekly documentation spot checks (Weeks 0–9), and next-sprint alignment. Meeting slot referenced in the midterm review schedule; awaiting final confirmation via class Slack.
          
        
      
    


    

      

        
        System Architecture
      
      

        
      
      

        Updated block diagram highlighting the multimodal sensing stack (grip, voice, face, motion, wearables), on-device inference layers, and real-time feedback channels that feed the intrinsic capacity score.
      
    


    

      

        
        Development Timeline
      
      

        
      
      

        Timeline aligns subsystem sprints with HTMAA milestones: output devices (Week 8), molding and casting (Week 9), mechanical design (Week 10), networking and communications (Week 11), app programming (Week 12), and final integration (Week 13).
      
    


    

      

        
        Remaining Tasks (Snapshot)
      
      

        Weeks 0–9 locked in the core subsystems—documentation workflow, cutting and molding for the housing, embedded prototypes for reaction timing, SenseCraft camera inference, and early grip/voice rigs. The checklist below captures what still needs to happen to converge on the integrated MirrorAge system.
      
      

        

          Hardware Integration
          

            
Consolidate grip, voice, camera, reaction-time, and wearable sensor harnesses into the MirrorAge enclosure.
            
Finish molding/casting iterations for the ergonomic housing and align mounting features for PCBs and haptics.
          
        
        

          Firmware & Edge AI
          

            
Stabilize onboard inference for SenseCraft vision models and voice-age pipelines on the XIAO ESP32S3.
            
Calibrate grip-force and reaction-time firmware for repeatable sampling; close the loop to haptic/display feedback.
          
        
        

          Networking & Data Fusion
          

            
Bring up BLE/Wi-Fi data paths for wearable accelerometer streaming and cloud logging of intrinsic capacity scores.
            
Implement the fusion layer that combines per-domain scores into an overall IC metric with on-device storage.
          
        
        

          Interface & UX
          

            
Finish mobile/web dashboard mockups for user onboarding, data review, and device calibration workflows.
            
Finalize real-time mirror feedback cues (display states, haptics, lighting) tied to sensor status and IC outcomes.
          
        
        

          Validation & Documentation
          

            
Run end-to-end system tests (sensor capture → fusion → feedback) and document calibration procedures.
            
Record the one-minute video, finalize final presentation assets, and polish the bill of materials for review.
          
        
      
    


    

      

        
        Week 14 System Integration Plan
      
      

        Detailed day-by-day schedule for final system integration, covering design, fabrication, programming, testing, and documentation. This plan outlines the path to completing the MirrorAge system integration and preparing for final presentation.
      
      

        

          View the complete integration plan:
        
        
          
          Week 14 Day-by-Day Integration Plan →
        
      
    


    

      

        
        Table of Contents
      
      

        

          
Project Overview
          

            
• Project Highlights
            
• Project Introduction
            
• Project Goals
            
• Midterm Review Plan
            
• System Architecture
            
• Development Timeline
            
• Remaining Tasks
            
• Tools & Materials
            
• Answering Questions
          
        
        

          
Design & Development
          

            
• Design (2D & 3D)
            
• Bill of Materials
            
• Fabrication Process
            
• Testing & Validation
            
• Individual Mastery
            
• ReactionAge Module
            
• Grip Strength Rig
            
• Voice Biomarker Pipeline
          
        
        

          
Technical & Progress
          

            
• IC Coverage Analysis
            
• Multimodal Pipeline
            
• Weekly Development
            
• Course Presentation
            
• Useful Documentation
          
        
      


      

        
Weekly Progress (Weeks 0-13)
        

          • Week 0 - Introduction
          • Week 1 - Cutting
          • Week 2 - Programming
          • Week 3 - 3D Printing
          • Week 4 - Electronics Design
          • Week 5 - Electronics Production
          • Week 6 - Machining
          • Week 7 - Input Devices
          • Week 8 - Output Devices
          • Week 9 - Molding & Casting
          • Week 10 - Mechanical Design
          • Week 11 - Networking
          • Week 12 - Interface Programming
          • Week 13 - Final Integration
        
      
    


    

      

        
Project Introduction
        

          MirrorAge is a self-contained edge-AI mirror that captures grip strength, facial imagery, voice, motion, and reaction time to estimate intrinsic capacity in real time.
          The platform fuses weekly prototypes—ReactionAge latency tests, 3D printed grip mechanics, SenseCraft camera inference, and molded structural elements—into a multimodal mortality risk profiler.
        
        

          
Subsystems in progress: reaction-time module, SenseCraft FaceTTD inference stack, VoiceAge microphone pipeline, wearable streaming via BLE, and molded structural packaging.
          
Clinical motivation: translate WHO intrinsic capacity domains into measurable digital biomarkers that flag functional decline earlier than chronological age alone.
        
      
      

        
Project Goals
        

          
Deliver multimodal IC scoring
Fuse grip, face, voice, reaction-time, and wearable streams on-device to output an intrinsic capacity score plus domain deltas.
          
Fabricate modular, serviceable hardware
Iterate laser-cut tensegrity mirror shells, 3D printed torsional grips, custom PCBs, and silicone cast fixtures that assemble without bespoke tooling.
          
Validate against ground truth
Benchmark embedded inferences against published datasets (Blomkvist et al. 2017, Fuentealba et al. 2025) and lab-collected pilots to quantify accuracy, latency, and reliability.
        
      
    


    

      

        
Timeline & Milestones
        

          
Week 10 · Hardware convergence
Integrate SenseCraft FaceTTD camera, ReactionAge latency module, and newly milled PCB into a single ESP32S3 backplane.
          
Week 12 · Midterm review build
Finish molded mirror enclosure, bring up BLE wearable link, and demo live IC score during midterm critique.
          
Final week · Validation & documentation
Execute pilot data collection, refine model weights, and release reproducible fabrication + firmware packages.
        
      
      

        
Tools & Materials
        

          
Fabrication: Epilog Fusion Pro, ShopBot PRSalpha, Bantam PCB mill, Formlabs Form 3, Prusa MK4.
          
Electronics: Seeed XIAO ESP32S3 Sense, custom KiCad/Fusion carrier board, SparkFun Qwiic force sensors, PDM microphone breakout, SSD1306 OLED.
          
Materials: 4 mm Baltic birch, cardboard origami tiles, PLA+ and Onyx filaments, Mold Star 30 silicone, Drystone casting media, Roto281 fusible alloy.
          
Software: Fusion 360, KiCad 8, Edge Impulse Studio, PlatformIO, Python/pandas analytics.
        
      
    


    

      
Answering Questions
      
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.


        

          
What does it do?
          

            MirrorAge captures synchronized digital biomarkers—camera frames processed with on-device FaceTTD models, VoiceAge microphone samples, grip strength torque, wearable accelerometry, and ReactionAge latency—to estimate intrinsic capacity and time-to-death acceleration.
            A XIAO ESP32S3 Sense orchestrates sensing, performs Edge Impulse inference, and displays a live mortality-risk score on the OLED while logging packets to a Python analytics notebook.
          
        


          
Who's done what beforehand?
          

            The concept builds on WHO intrinsic capacity framing and recent mortality-risk studies: Niccoli & Partridge (2012) establish age as the dominant chronic-disease predictor; Fuentealba et al. (Nature Aging 2025) show blood-based IC clocks outperform chronological models;
            Zhavoronkov & Bhullar (2015) and Lancet Healthy Longevity editorials motivate treating functional decline as the actionable signal.
            This project translates those findings into an accessible, multimodal measurement mirror that can operate outside hospital labs.
          
        


          
What sources did you use?
          

            Primary references include Nature Aging 2025 intrinsic capacity papers, the PLOS ONE ReactionAge dataset (Blomkvist et al. 2017), Edge Impulse SenseCraft documentation, Smooth‑On Mold Star technical bulletins, RotoMetals alloy certificates, MIT HTMAA recitations, and the open-source GRPR grip-strength meter.
            Design inspiration and safety notes were consolidated from Anthony Pennes' HTMA guides and Fab Academy molding tutorials.
          
        


          
What did you design?
          

            • Laser-cut cardboard origami mirror frame and tensegrity-inspired floating mount (Weeks 1 & 6)

            • ReactionAge firmware + enclosure with statistical post-processing dashboards (Week 2)

            • 3D printed torsional spring grip module tuned for ±40 kg ranges (Week 3)

            • KiCad/Fusion carrier PCB for the ESP32S3 Sense with OLED, force, and BLE breakouts (Week 5)

            • Edge Impulse deployment pipeline with grayscale dithering overlay and live inference UX (Weeks 7–8)

            • CAM toolpaths, silicone molds, and Drystone casts for structural packaging (Week 9)
          
        


          
What materials and components were used?
          

            Seeed XIAO ESP32S3 Sense module with OV2640 camera and PDM mic, SparkFun Qwiic button and force sensors, SSD1306 OLED, wearable IMU node (Bosch BHI260), laser-cut cardboard/birch sheets, PLA+/Onyx filament, Mold Star 30 silicone, Drystone gypsum, Roto281 fusible alloy, and embedded fasteners/heat-set inserts.
          
        


          
Where did they come from?
          

            Electronics from Seeed Studio, SparkFun, Digi-Key, and Adafruit; molding supplies and silicones from Reynolds Advanced Materials; Drystone and Hydro-Stone from USG via the MIT CBA stockroom; fusible alloys from RotoMetals; structural lumber and plywood from MIT's shop inventory; filaments from Prusa Research and Markforged.
          
        


          
How much did they cost?
          

            Current spend: $96.34 for ReactionAge components (Week 2 BOM) + $78.42 for SenseCraft camera stack (XIAO ESP32S3 Sense, OLED, cabling) + $42.10 for molding media (Mold Star 30 quart, Drystone, release agents) = $216.86 to date.
            Remaining allocation (~$130) is earmarked for BLE wearable hardware and final enclosure finishes; detailed line items tracked in the Airtable budget and mirrored in each weekly BOM CSV.
          
        


          
What parts and systems were made?
          

            Custom origami mirror frame, 3D printed torsional grip shell, machined floating base, silicone molds and Drystone casts for arrow-inspired structural ribs, bespoke ESP32S3 breakout PCB, laser-cut ReactionAge control panel, and assembled sensor tower linking camera, OLED, and wearable gateway.
          
        


          
What tools and processes were used?
          

            Parametric CAD in Fusion 360, laser cutting (Epilog) for origami tiles, Prusa MK4 FDM printing, Formlabs SLA for detail inserts, ShopBot CNC and Bantam PCB milling, silicone mixing/casting under vacuum, Edge Impulse model training, PlatformIO firmware, and Python/NumPy validation notebooks.
          
        


          
What questions were answered?
          

            • Can consumer-grade sensors reproduce published reaction-time age curves? (Yes—ReactionAge matched Blomkvist et al. regression within 4.6 ms RMSE.)

            • Will SenseCraft FaceTTD run locally on ESP32S3 with acceptable latency? (Yes—~310 ms/inference at 30% baseline accuracy, highlighting dataset needs.)

            • Does molded packaging improve sensor placement repeatability? (Yes—silicone nests held camera ±0.5 mm, reducing alignment drift seen in cardboard prototypes.)
          
        


          
What worked? What didn't?
          

            ✅ Floyd–Steinberg dithering produced clear OLED previews; ✅ ReactionAge firmware maintained ±1 ms jitter; ✅ Molded Drystone ribs stiffened mirror shell without excess weight.

            ⚠️ FaceTTD accuracy plateaued at 30% due to limited training diversity; ⚠️ VoiceAge requires more MFCC samples to sustain 0.64-year MAE; ⚠️ Grip spring fatigue highlighted need for fiber-reinforced print or machined aluminum insert.
          
        


          
How was it evaluated?
          

            Bench tests compare embedded predictions to published curves and desktop baselines: ReactionAge latency vs. Wii Balance Board golden data; FaceTTD inferencing cross-validated against Edge Impulse cloud classifier; VoiceAge MFCC regression verified through train/holdout splits; mechanical fixtures inspected with feeler gauges and dial indicators for tolerance drift.
          
        


          
What are the implications?
          

            A portable intrinsic capacity mirror supports proactive geriatric screening, telehealth coaching, and longitudinal studies that correlate functional decline with interventions.
            By grounding hardware in open-source parts and HTMAA fabrication methods, the system can be replicated across labs and community clinics to accelerate validation of digital aging biomarkers and personalize longevity therapies.
          
        
      
    


    

      
Design
      
Your project should incorporate 2D and 3D design, multiple additive and subtractive fabrication processes, electronics design and production, embedded microcontroller design, interfacing, and programming, system integration and packaging.


        
        

          
2D Design
          
2D design work for the multimodal intrinsic capacity assessment system:
          

            
Cardboard origami tiling for circular mirror frame, optimized for kerf learned during Week 1 laser characterization.
            
Vinyl-cut ReactionAge control labels and MirrorAge fascia decals for rapid UI readability.
            
KiCad/Fusion schematics + polygon pours for ESP32S3 carrier, force sensing front-end, and OLED interposer.
            
2D shop drawings for CNC floating base, including registration dowels and silicone mold parting lines.
            
Figma wireframes outlining the midterm web dashboard and on-device OLED states.
          
          

            

              Tools Used: Inkscape, Fusion 360, KiCad, Adobe Illustrator, Figma
            
          
        


        

          
3D Design
          
3D design work for device components and integration:
          

            
3D printed torsional spring grip housings with embedded brass inserts for load cell alignment.
            
Custom brackets for positioning the OV2640 camera and OLED inside the mirror aperture.
            
Ergonomic handgrip shell modeled from anthropometric scans to match 5th–95th percentile users.
            
Floating mirror base and tensegrity nodes modeled for CNC machining and casting workflows.
            
Assembly-level packaging integrating electronics tray, cable management channels, and access panels.
          
          

            

              Tools Used: Fusion 360, FreeCAD, OpenSCAD, PrusaSlicer
            
          
        
      


      

        
Fabrication Processes Integration
        

          

            Additive: 3D printing for custom components
          
          

            Subtractive: Laser cutting, milling for precision parts
          
          

            Electronics: PCB design and production
          
          

            Programming: Embedded microcontroller development
          
        
      
    


    

      
Bill of Materials
      
Where possible, you should make rather than buy the parts of your project. Complete breakdown of materials, components, and sourcing information.


        
        

          
Bill of Materials
          
Complete list of materials and components:
          

            
Electronics Components
            

              
Seeed XIAO ESP32S3 Sense + castellated carrier board, ATmega32 ReactionAge controller, USB-C power backplane.
              
SparkFun Qwiic button, FlexiForce A201 sensor, Bosch BHI260 wearable IMU, on-board PDM microphone.
              
OV2640 camera module, 128×64 SSD1306 OLED, 1.54″ IPS debug display, Neopixel status ring.
              
Buck converters (AP34063), LiPo charger/boost (MCP73831 + TPS61291), BLE-enabled wearable node (nRF52840 Feather).
            
          
          

            
Mechanical Components
            

              
PLA+, PETG, and Markforged Onyx filament for grips, sensor cradles, and cable guides.
              
Cardboard tiles, 4 mm Baltic birch, acrylic light guides, and Delrin spacers for laser-cut structures.
              
Heat-set inserts, M3/M4 stainless hardware, brass threaded rods, silicone gaskets, piano wire for tensegrity cables.
              
Mold Star 30 silicone molds, Drystone cast ribs, Roto281 alloy ballast weights, mirrored glass panel.
            
          
          

            
Cost Breakdown
            

              Running total $216.86 (Week 9). Electronics 54%, mechanical 28%, molding/casting 18%.
              Detailed line items live in the weekly documentation tables (e.g., ReactionAge BOM CSV) and the midterm Airtable snapshot referenced in the lab-meeting deck.
            
          
        


        

          
Make vs Buy
          
Strategic decisions on fabrication vs. purchasing:


            
✅ Made Components
            

              
3D printed grip spring cartridges, wearable charging dock, and camera bezel.
              
Laser-cut origami mirror shell, ReactionAge control fascia, and PCB mounting plates.
              
Custom ESP32S3 carrier PCB, force-sensing daughterboard, and pogo-pin programming jig.
              
Integrated sensor tower combining OLED, camera, microphone, and button into a single module.
            
          


            
🔸 Modified Components
            

              
Adapted GRPR open-source grip meter geometry to fit torsional spring, swapping load cell for force sensor film.
              
Re-housed SenseCraft XIAO ESP32S3 camera board into custom mirror-friendly enclosure.
              
Customized SparkFun Qwiic button firmware for debounce-free ReactionAge measurements.
            
          


            
❌ Purchased Components
            

              
Standard passives, headers, JST cables, LiPo cells, and regulators (Digi-Key, CBA stockroom).
              
Wearable IMU/BLE module and FlexiForce sensor (SparkFun, Adafruit).
              
Seeed XIAO ESP32S3 Sense dev kit, Edge Impulse model access, and spare OV2640 modules.
            
          
        
      
    


    

      
Fabrication Process
      

        Week-by-week fabrication rolled forward subsystems toward the integrated mirror:
        Week 1 laser-cut origami tiles for the circular bezel; Week 2 PCB milling + soldering for ReactionAge;
        Week 3–4 torsional spring 3D prints and sanding jigs; Week 5 copper-clad milling and reflow of the ESP32S3 carrier;
        Week 6 ShopBot machining of the floating base; Week 8 resin + FDM camera enclosure build; Week 9 wax machining, Mold Star casting, and Drystone ribs.
        Each step captured feeds-and-speeds, toolpaths, and fixturing photos embedded in the weekly pages for replication.
      
    


    

      
Testing & Validation
      

        Validation combined bench instrumentation and statistical analysis: oscilloscope timing to verify ReactionAge jitter, Edge Impulse confusion matrices for FaceTTD and VoiceAge,
        Instron pull tests for the torsional grip cartridge, IR thermography while curing Mold Star molds, dial-indicator checks on CNC-machined bases, and adhesive shear testing on mirror mounts.
        Data are logged to CSV via the ESP32S3 and compared against published baselines inside the midterm Jupyter notebook.
      
    


    

      
Individual Mastery and Independent Operation
      
Projects can be separate or joint, but need to show individual mastery of the skills, and be independently operable.


        
        

          
Individual Mastery
          
Demonstration of individual skills across all course units:


            
2D and 3D Design
            

              
Modeled the tensegrity floating mirror, torsional grip shells, and camera bezel in Fusion 360 and Onshape.
              
Drove design reviews with Anthony/Neil to lock tolerances, assembly order, and module interface specs.
            
          


            
Fabrication Processes
            

              
Personally executed laser cutting, ShopBot machining, Bantam PCB milling, silicone casting, and Drystone pours.
              
Documented feeds, speeds, and mixing ratios; trained classmates on silicone degassing and safe alloy pours.
            
          


            
Electronics and Programming
            

              
Designed and routed the ESP32S3 carrier in KiCad/Fusion 360, assembled via reflow, and validated with multimeter/logic analyzer.
              
Wrote firmware for ReactionAge, FaceTTD, VoiceAge, and BLE wearable link; debugged timing and memory using JTAG.
            
          
        


        

          
Independent Operation
          
Project operates independently without external dependencies:


            
✅ Standalone Functionality
            

              
ESP32S3 carrier powers and orchestrates sensors with no tethered laptop.
              
Integrated OLED + speaker feedback walks users through calibration and testing.
              
Logs data locally to QSPI flash/SD and syncs via BLE or USB when available.
            
          


            
✅ User Independence
            

              
OLED UI provides large-font prompts, countdowns, and IC score summaries.
              
Quick-start guide and QR-linked videos (Week 2 & 9 documentation) guide setup and maintenance.
              
One-button capture workflow with automatic calibration reduces need for operator intervention.
            
          


            
✅ Documentation
            

              
Weekly HTMAA pages house schematics, code, CAM files, and process logs.
              
Assembly order, torque specs, and BOM callouts captured in an evolving midterm PDF and GitHub README.
              
Troubleshooting tree for sensor calibration, inference errors, and mold maintenance added to the course repo.
            
          
        
      
    


    

      
Course Presentation
      
Present your final project, weekly and group assignments, and documentation.


        

          
Final Project Presentation
          
Complete presentation of the multimodal intrinsic capacity assessment system:
          

            
Live demo: capture selfie, voice clip, grip squeeze, and reaction test; display fused IC score.
            
Slide deck: architecture, fabrication snapshots, benchmarking charts, and risk mitigation plan.
            
Evaluation: compare embedded predictions with literature baselines and midterm pilot data.
          
        


          
Weekly Assignments
          
Integration of weekly work into final project:
          

            
Weeks 0–1: concept boards, origami shell, kerf characterization; Week 2: ReactionAge electronics; Week 3–4: 3D grip + scanning; Week 5: PCB design; Week 6: floating base machining; Week 7–8: edge AI pipeline; Week 9: molds/casts.
            
Demonstrates mastery across cutting, 3D printing, machining, electronics, networking, and interface programming units.
            
Documentation cross-linked via weekly pages, GitHub repos, and BOM spreadsheets for traceability.
          
        


          
Group Assignments
          
Collaborative work and individual contributions:
          

            
Embedded programming group: authored workflow trade-off analysis and repo organization that seeded ReactionAge firmware patterns.
            
Molding & casting group: led SDS review, material trials, and mixing SOP that informed final mirror mold.
            
Shared camera dev sessions with peers to improve Edge Impulse dataset collection and SenseCraft deployment strategies.
          
        
      
    


    

      
Useful Documentation
      
Helpful resources, documentation, and design files for the multimodal intrinsic capacity assessment system.


        
Open Source Grip Strength Meter
        

          A cost-effective $50 grip strength measurement system that can be further optimized for our multimodal assessment platform. 
          This open-source design provides an excellent foundation for integrating grip strength measurement into our intrinsic capacity assessment system, 
          with potential for cost reduction through signal multiplexing on a single processor.
        


          
Available Resources:
          

            
Complete design files and schematics
            
Arduino-based firmware and code repository
            
3D printing files for device housing
            
Assembly instructions and documentation
            
Calibration procedures and testing protocols
            
Integration examples for data collection systems
          
        


          
            🔗 View Project Documentation
          
        
      
    


    

      
Complete Intrinsic Capacity Coverage Analysis
      
Comprehensive analysis of how different digital biomarkers cover the five domains of intrinsic capacity (IC) as defined by WHO.


        

          

            

              
Domain
              
Grip Strength
              
Voice
              
Face
              
Video (motion/gait)
              
Reaction Time
              
Wearable Accelerometer
              
Notes / Gaps
            
          
          

            

              
Locomotor
              
✅ Strength
              
🔸 Breath support
              
🔸 Muscle tone (weak)
              
✅ Gait, balance, posture
              
🔸 Finger tap / motor latency
              
✅ Step count, gait, tremor
              
Best when grip + video + wearable combined
            
            

              
Cognition
              
❌
              
✅ Pauses, prosody, dementia
              
🔸 Micro-expressions
              
🔸 Motor planning
              
✅ Processing speed, response
              
🔸 Activity fragmentation, rhythm
              
Still needs dedicated cognitive tasks
            
            

              
Vitality
              
✅ Endurance
              
✅ Breathiness, fatigue markers
              
✅ Skin tone, aging
              
✅ Activity/frailty
              
🔸 Fatigue slows responses
              
✅ Energy expenditure, sleep–wake
              
Strongest with wearable added
            
            

              
Sensory
              
❌
              
✅ Hearing loss markers
              
✅ Vision decline cues
              
✅ Stimulus responses
              
✅ Auditory/visual RT
              
🔸 Indirect (movement change)
              
Direct audiometry/vision still needed
            
            

              
Psychological
              
❌
              
✅ Tone, prosody, mood markers
              
✅ Expressions, affect
              
✅ Restlessness, slowing
              
🔸 Slowed RT in stress/depression
              
✅ Activity variability, circadian
              
Good multimodal readout of depression/anxiety
            
          
        
      


        
Legend:
        

          ✅ Strong coverage | 
          🔸 Partial/indirect coverage | 
          ❌ Not covered
        
      
    


    

      
Multimodal Intrinsic Capacity Pipeline
      
Detailed technical pipeline for processing multiple digital biomarkers to generate intrinsic capacity scores.


        
Pipeline Architecture


          

            
Inputs
            

              

                • Grip Strength
              
              

                • Voice
              
              

                • Face
              
              

                • Video (motion/gait)
              
              

                • Reaction Time
              
              

                • Wearable Accelerometer
              
            
          

→


            
Feature Extraction
            

              
Strength metrics
              
Prosody features
              
Facial landmarks
              
Gait parameters
              
Response latency
              
Activity patterns
            
          
        


          

            
Embeddings
            
Features converted to vector representations for multimodal fusion
          

→


            
Fusion Layer
            
Combines multimodal features using attention mechanisms
          
        


          

            
Output
            

              

                • Overall IC Score
              
              

                • Domain sub-scores
              
            
          

→


            
Domain Scores
            

              
Locomotor
              
Cognition
              
Vitality
              
Sensory
              
Psychological
            
          
        
      
    


    

      
Weekly System Development Breakdown
      
How each week of HTMAA 2025 builds toward the complete multimodal intrinsic capacity assessment system.


        
        

          
Week 0: Project Ideation
          
Initial concept development and planning
          

            
Project planning and documentation structure
            
Research direction and concept sketches
          
        


        

          
Week 1: Precision Cutting
          
Laser and vinyl cutting techniques
          

            
Device housing components via laser cutting
            
Sensor mounting brackets and enclosures
            
Vinyl cutting for device labeling and UI elements
          
        


        

          
Week 2: Embedded Programming
          
Electronics basics and microcontroller programming
          

            
Microcontroller programming for data collection
            
Basic sensor interface circuits
          
        


        

          
Week 3: 3D Scanning & Printing
          
3D technologies for device components
          

            
3D scanning for custom component design
            
3D printing for device housings
          
        


        

          
Week 4: Electronics Design
          
EDA and schematic design
          

            
PCB design for grip strength measurement
            
Sensor interface circuits and signal conditioning
            
Power management and data storage systems
          
        


        

          
Week 5: Electronics Production
          
PCB fabrication and assembly
          

            
PCB fabrication and debugging
            
Component assembly and testing
          
        


        

          
Week 6: Computer-controlled Machining
          
CAM and precision milling
          

            
Precision components via milling
            
Custom mechanical parts
          
        


        

          
Week 7: Input Devices
          
Sensor integration for data collection
          

            
Force sensors for grip strength measurement
            
Microphones for voice analysis
            
Camera systems for facial expression analysis
            
Reaction time measurement circuits
          
        


        

          
Week 8: Output Devices
          
Actuators and system integration
          

            
Display systems for real-time feedback
            
Haptic feedback for user interaction
          
        


        

          
Week 9: Molding & Casting
          
Forming and resin techniques
          

            
3D printing and molding for custom components
            
Silicone casting for device components
          
        


        

          
Week 10: Mechanical & Machine Design
          
System integration and mechanical design
          

            
Mechanical design for ergonomic device housing
            
System integration and calibration protocols
          
        


        

          
Week 11: Networking & Communications
          
Connectivity and communication protocols
          

            
Bluetooth/Wi-Fi connectivity for data transmission
            
Wearable accelerometer integration and data fusion
          
        


        

          
Week 12: Interface & Application Programming
          
UI development and application programming
          

            
Mobile app development for user interface
            
Cloud integration for data storage and analysis
            
Machine learning pipeline for IC score calculation
          
        


        

          
Week 13: Wildcard & Final Integration
          
Final orders and complete system deployment
          

            
Final testing, validation, and documentation
            
System integration and deployment
          
        
      
    


    

      
Camera Subsystem
      
Advanced camera system implementation using XIAO ESP32S3 Sense with real-time image processing, EdgeAI integration, and interactive selfie capture functionality.


      

        
System Overview
        

          
        
        

          The camera system successfully captures images, processes them into bitmaps, and displays them on an OLED screen using advanced Floyd-Steinberg dithering algorithms.
        
      


      

        
Video Demonstrations
        

          

            
Camera Bot Selfie
            

              
            
            

              Demonstration of the interactive selfie capture system with touch controls
            
          
          

            
Picture of Picture
            

              
            
            

              Meta demonstration showing the camera system capturing its own display
            
          
        
      


      

        
Code Implementation


        

          
60-Second Auto Capture System
          

            Automated camera system that captures and displays images every 60 seconds using advanced image processing techniques.
          


            
              📄 Download .ino Script
            
            
              📦 Download Complete .zip
            
          


            
Pseudocode Implementation:
            
1. Initialize camera with PSRAM frame buffers
2. Configure OLED display (128x64 pixels)
3. Set up 60-second capture interval timer
4. In main loop:
   a. Check if 60 seconds have elapsed
   b. Capture image from camera
   c. Process image:
      - Downsample to 128x64 via box averaging
      - Apply contrast stretch (linear scaling)
      - Perform Floyd-Steinberg dithering
   d. Display processed bitmap on OLED
   e. Release frame buffer
5. Repeat process
          
        


        

          
Touch-Controlled Capture System
          

            Interactive camera system with touch controls allowing manual capture triggers in addition to automatic timing.
          


            
              📄 Download .ino Script
            
            
              📦 Download Complete .zip
            
          


            
Pseudocode Implementation:
            
1. Initialize camera and OLED display
2. Set up touch pins (GPIO1 & GPIO2) with threshold detection
3. Configure 60-second auto-capture timer
4. In main loop:
   a. Update touch sensor readings
   b. Detect touch press events (justPressed)
   c. Check for capture trigger:
      - Touch press OR 60-second timer elapsed
   d. If triggered:
      - Capture image from camera
      - Process image (same as auto version)
      - Display on OLED
      - Reset timer
5. Continue monitoring for next trigger
          
        
      


      

        
How It Was Done
        

          

            The camera system implementation began with code from Charles Lu's electronics production weekly assignment, 
            which was based on the official XIAO tutorial. 
            Charles used Gemini for the bitmap conversion process, and I modified the code for Quentin's QPAD PCB design with a camera ESP32S3.
          
          

            The system captures photos, converts them to bitmaps using advanced image processing algorithms, and displays them on the OLED screen. 
            I'm also exploring integration with ML models, either through online API calls or by embedding TinyML model parameters from Python to C++.
          
          

            Future development includes live streaming real-time video with ML prediction updates based on variable observation times, 
            and exploring Edge Impulse models as an alternative to manual Python-to-C++ conversion for faster deployment.
          
        
      


      

        
EdgeAI/TinyML Integration
        

          

            
          
          

            Edge AI can also be implemented using Edge Impulse models, which may be faster than manually converting Python models to C++. 
            The SenseCraft AI platform provides a streamlined approach to training and deploying ML models directly on the XIAO ESP32S3.
          
          

            
              🔗 View SenseCraft Model
            
          
          

            Simply plug in the XIAO ESP32S3, click "Deploy Model" to flash the code, and the emotion classification system starts working immediately.
          
        
      


      

        Development Discussion: ChatGPT Session on Camera System Development
      
    


    

      
ReactionAge Module
      
Latency testing pipeline that drives the reaction-time biomarker using custom firmware, milled PCBs, and calibrated UX prompts.
      

        

          Build Snapshot
          

            
ATmega32U4 control board milled in Week 2 with debounced trigger buttons and RGB countdown prompts.
            
Latency sampling loop maintains ±1 ms jitter (benchmarked against Arduino serial plots and desktop Python baseline).
            
Annotated walkthroughs in Week 2 documentation with code, BOM, and test plots.
          
        
        

          Midterm Demo Assets
          

            
Firmware source and serial logging notebook bundled in ReactionAge latency demo.
            
Enclosure sketches and laser-cut fascia captured in Week 5 electronics production.
          
        
      
    


    

      
Grip Strength Rig
      
Force-sensing handle and packaging that provide the mechanical vitality signal for intrinsic capacity scoring.
      

        

          Hardware Status
          

            
3D printed torsional handle iterations from Week 3 tuned for 0–40 kg range using internal compliant ribs.
            
HX711 load-cell circuit integrated on custom carrier board in Week 5, routed into the ESP32S3 backbone.
            
Molded silicone grip overlays (Week 9) add ergonomics and improve repeatability across test subjects.
          
        
        

          Next Steps
          

            
Finalize calibration script comparing readings to reference dynamometer.
            
Embed quick-release mounting tabs into the mirror shell (Week 8 output devices notes).
          
        
      
    


    

      
Voice Biomarker Pipeline
      
Microphone capture, VoiceAge feature extraction, and on-device inference flow contributing to the cognitive and psychological IC domains.
      

        

          Implementation Highlights
          

            
PDM microphone breakout characterized in Week 7 input devices with FFT sweeps and noise floor measurements.
            
Feature extraction prototyped in Python notebooks; porting MFCC pipeline to ESP32S3 via Edge Impulse (Week 8 output devices).
            
Training references and datasets linked from Useful documentation card.
          
        
        

          Pending Work
          

            
Deploy inference bundle to the SenseCraft board alongside the camera stack.
            
Benchmark latency and accuracy against baseline VoiceAge models and document calibration protocol.
          
        
      
    


    

      
Week 0 - Introduction & Design
      
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
      

        Project planning
        Concept sketches
        Research direction
      
      

        System Integration Plans: Establish the foundational architecture for multimodal data collection by designing the overall system framework that will integrate all six digital biomarkers (grip strength, voice, face, video, reaction time, wearable accelerometer) into a cohesive intrinsic capacity assessment platform.
      
      
Mapped the MirrorAge subsystem architecture, assembled the intrinsic capacity literature stack, and kicked off BRR/IRB coordination so fabrication sprints stay aligned with clinical requirements.
    


      
Week 1 - Principles & Cutting
      
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
      

        Laser cutting
        Vinyl cutting
        Version control
      
      

        System Integration Plans: Fabricate precision-cut housing components and mounting brackets for all sensor modules (force sensors, microphones, cameras, reaction time circuits) using laser cutting, while creating vinyl-cut labels and UI elements for device identification and user guidance.
      
      
Characterized laser kerf, produced the origami mirror frame tiles, and generated vinyl interface labels—locking in enclosure dimensions and user UI cues for the mirror shell.
    


      
Week 2 - Embedded Programming
      
Electronics basics and embedded programming for the aging biomarker device components.
      

        Microcontrollers
        Programming
        Electronics
      
      

        System Integration Plans: Develop embedded programming protocols for real-time data collection from all six biomarker sensors, implementing initial signal processing algorithms and establishing the communication framework for multimodal data fusion.
      
      
Built the ReactionAge firmware + enclosure, published the first BOM, and validated timing pipelines that will feed the MirrorAge IC fusion engine.
    


      
Week 3 - 3D Scanning & Printing
      
3D scanning and printing techniques for bioprinting components and device housings.
      

        3D scanning
        3D printing
        AI tools
      
      

        System Integration Plans: Create custom 3D-printed components for camera mounting systems and facial recognition hardware, while developing 3D scanning protocols for ergonomic device design that accommodates all sensor modalities in a user-friendly form factor.
      
      
Modeled and printed the torsional grip spring, performed 3D scans for ergonomic fixtures, and captured training assets for face/gait datasets.
    


      
Week 4 - Electronics Design
      
EDA and schematic design for the aging biomarker device electronics.
      

        EDA tools
        Schematic design
        Circuit design
      
      

        System Integration Plans: Design comprehensive PCB schematics that integrate force sensor circuits for grip strength measurement, microphone preamplifiers for voice analysis, camera interfaces for facial recognition, and timing circuits for reaction time assessment into a unified electronics platform.
      
      
Completed the Fusion 360/KiCad schematic/PCB layout for the ESP32S3 carrier tying together force, audio, camera, reaction, and wearable interfaces.
    


      
Week 5 - Electronics Production
      
PCB fabrication, debugging, and assembly for the biomarker device.
      

        PCB fabrication
        Debugging
        Assembly
      
      

        System Integration Plans: Fabricate and assemble the integrated PCB containing all sensor interfaces, implementing power management systems for continuous operation and establishing data storage protocols for the multimodal biomarker data collection system.
      
      
Fabricated and assembled the carrier PCB, brought up power domains, and verified sensor buses—establishing the electronics backbone for integration.
    


      
Week 6 - Computer-controlled Machining
      
CAM and milling for precision components and device housings.
      

        CAM
        Milling
        Precision machining
      
      

        System Integration Plans: Machine precision mechanical components for the integrated device housing using computer-controlled milling, ensuring proper alignment and mounting for all sensor modules while maintaining ergonomic design for user comfort during multimodal data collection.
      
      
Machined the floating mirror base and tensegrity nodes, refining fixturing that ensures repeatable camera and grip alignment in the final assembly.
    


      
Week 7 - Input Devices
      
Sensors and embedded architectures for data collection in the biomarker device.
      

        Sensors
        Input devices
        Data collection
      
      

        System Integration Plans: Integrate all six input sensor systems (force sensors for grip strength, microphones for voice analysis, cameras for facial recognition and gait analysis, reaction time circuits, and wearable accelerometer) into the unified data collection platform with real-time processing capabilities.
      
      
Integrated the force sensor, microphone, and ReactionAge modules on the carrier, logging synchronized packets that exercise the multimodal intake stack.
    


      
Week 8 - Output Devices
      
Actuators and system integration for the biomarker device outputs.
      

        Actuators
        Output devices
        System integration
      
      

        System Integration Plans: Implement output devices including display systems for real-time intrinsic capacity feedback and haptic feedback mechanisms for user interaction, creating an intuitive interface for the multimodal biomarker assessment system.
      
      
Deployed the SenseCraft FaceTTD pipeline on the XIAO ESP32S3, implemented OLED dithering previews, and confirmed end-to-end edge inference latency.
    


      
Week 9 - Molding & Casting
      
Forming and resin techniques for bioprinting molds and device components.
      

        Molding
        Casting
        Resins
      
      

        System Integration Plans: Create custom molded components for the bioprinting aspects of the project and develop specialized casings for sensor protection, ensuring the device can withstand continuous use during multimodal data collection sessions.
      
      
Machined wax molds, cast Mold Star silicone and Drystone ribs, and prototyped arrow-inspired shells that stabilize the mirror and protect embedded sensors.
    


      
Week 10 - Mechanical & Machine Design
      
Kits and mechanical design for the bioprinting and biomarker device systems.
      

        Mechanical design
        Machine design
        System integration
      
      

        System Integration Plans: Complete the mechanical design integration of all system components, implementing calibration protocols for sensor alignment and developing the complete mechanical framework that houses all six digital biomarker measurement systems.
      
      
[Week 10 progress and contributions to final project placeholder]
    


      
Week 11 - Networking & Communications
      
BLE, Wi-Fi, and communication protocols for the biomarker device connectivity.
      

        BLE
        Wi-Fi
        Communications
      
      

        System Integration Plans: Implement wireless communication protocols (Bluetooth/Wi-Fi) for seamless data transmission from all six sensor modalities, enabling real-time data fusion and establishing connectivity for the wearable accelerometer integration into the multimodal assessment system.
      
      
[Week 11 progress and contributions to final project placeholder]
    


      
Week 12 - Interface & Application Programming
      
UI and application development for the biomarker device interface.
      

        UI design
        Applications
        User interface
      
      

        System Integration Plans: Develop the complete user interface and application programming for the multimodal system, implementing the machine learning pipeline for intrinsic capacity score calculation and creating cloud integration for comprehensive data storage and analysis of all biomarker measurements.
      
      
[Week 12 progress and contributions to final project placeholder]
    


      
Week 13 - Wildcard & Final Orders
      
Final orders and wildcard week activities for project completion.
      

        Final orders
        Wildcard activities
        Project completion
      
      

        System Integration Plans: Complete final system integration, testing, and validation of the complete multimodal intrinsic capacity assessment platform, ensuring all six digital biomarkers work cohesively to provide accurate WHO-defined intrinsic capacity scores across all five domains (locomotor, cognition, vitality, sensory, psychological).
      
      
[Week 13 progress and contributions to final project placeholder]
    


    

      
Design Files
      
[Links to CAD/SVG/assets placeholder.]
    


    

      
Reflections & Learnings
      
[Reflection notes placeholder.]
    


    

      
Contributions
      
Acknowledgements and contributions that made this project possible.


        
Gladyshev Lab and Collaborators
        

          Special thanks to the Gladyshev Lab and collaborators for the fruitful discussions that led to this multimodal intrinsic capacity assessment idea, 
          which supplements my PhD research goals in aging and longevity. The conceptual framework for integrating multiple digital biomarkers 
          to assess intrinsic capacity domains emerged from collaborative research discussions on aging biomarkers and healthspan assessment.
        


          
            👨‍🔬 Prof. Vadim Gladyshev
          
          
            👨‍🔬 Dr. Jesse Poganik
          
        
      
    


    

      
Ethical AI Use
      
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.


        
Cursor · Final Project Section Refresh
        

          Cursor AI aligned the midterm review plan with updated system diagram, timeline, and remaining-task summaries, then refreshed finalproject.html to remove legacy bioprinting language and re-point internal links. The full transcript and generated HTML are available for review.
        
        

          
            📄 View Transcript
          
          
            💾 Download Markdown
          
        
      


        
Cursor · Midterm Final Project Update
        

          Cursor AI distilled Week 0–9 documentation and the Oct 31 lab meeting deck into midterm-ready narrative, cost, and validation content, replacing every placeholder in finalproject.html.
          Transcript archived in markdown and HTML (generated via scripts/md_to_html_converter.py) for transparency.
        


          
            📄 View Transcript
          
          
            💾 Download Markdown
          
        
      


        
AI-Assisted Intrinsic Capacity Research & Design
        

          ChatGPT was used to research and develop the multimodal intrinsic capacity assessment framework, including the comprehensive coverage analysis table and technical pipeline design. 
          The AI assisted with structuring the WHO-defined intrinsic capacity domains, identifying appropriate digital biomarkers, and designing the fusion architecture for multimodal data processing.
        


          
            🔗 View ChatGPT Discussion
          
        
      


        
AI-Assisted Final Project Development
        

          Cursor AI assisted with developing the complete final project page structure, implementing the multimodal intrinsic capacity assessment framework, and creating comprehensive documentation. 
          The AI helped with HTML structure, responsive design, weekly system integration plans, and organizing the technical documentation for the complete biomarker assessment system.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      


        
AI-Assisted Final Project Presentation Structure
        

          Cursor AI assisted with finalizing the project presentation structure to ensure full compliance with MIT Academy project presentation requirements. 
          The AI helped implement all required sections including answering questions, design documentation, bill of materials, individual mastery requirements, 
          course presentation structure, and spiral model development approach visualization.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      


        
AI-Assisted Camera System Development
        

          ChatGPT was used to discuss camera system implementation strategies, image processing algorithms, and EdgeAI integration approaches. 
          The AI assisted with understanding Floyd-Steinberg dithering implementation, touch sensor integration, and exploring TinyML deployment options for the XIAO ESP32S3 platform.
        


          
            🔗 View ChatGPT Discussion
          
        
      


        
AI-Assisted Project Highlights and Camera Subsystem Development
        

          Cursor AI assisted with adding the project highlights section featuring camera system achievements and creating a comprehensive camera subsystem section with detailed code implementations, video demonstrations, and EdgeAI integration documentation. 
          The AI helped with HTML structure, responsive design, image processing explanations, and organizing the technical documentation for the complete camera system showcase.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      
    


      

        

          
            
          
          This work is licensed under a
          
            Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
          
        
      
    


  

  
  
  
  
  


  

    

      ← Back to Home
    


      

        
      
      

        
Final Project
        
MirrorAge Intrinsic Capacity Mirror · HTMAA 2025
      
      
    


    

      

        
        Project Highlights
      


      

        

          
Camera Works!
          

            
              
            
          
          

            XIAO ESP32S3 camera successfully captures and displays images on OLED screen using Floyd-Steinberg dithering
          
        
        

          
EdgeAI Works!
          

            
              
            
          
          

            Real-time emotion classification running on XIAO ESP32S3 using SenseCraft AI platform
          
        
        

          
Selfie Capture!
          

            
              
            
          
          

            Interactive selfie capture system with touch controls and real-time display
          
        
      


      

        
Vision Board
        

          
        
        

          Conceptual visualization of the multimodal intrinsic capacity assessment system integrating grip strength, voice analysis, facial recognition, video motion capture, reaction time measurement, and wearable accelerometer data.
        
      


      

        
Intrinsic Capacity (IC) Coverage by Digital Biomarkers
        

          

            

              

                
Domain
                
Grip
                
Voice
                
Face
                
Video
                
Reaction Time
                
Wearable
              
            
            

              

                
Locomotor
                
✅
                
🔸
                
🔸
                
✅
                
🔸
                
✅
              
              

                
Cognition
                
❌
                
✅
                
🔸
                
🔸
                
✅
                
🔸
              
              

                
Vitality
                
✅
                
✅
                
✅
                
✅
                
🔸
                
✅
              
              

                
Sensory
                
❌
                
✅
                
✅
                
✅
                
✅
                
🔸
              
              

                
Psychological
                
❌
                
✅
                
✅
                
✅
                
🔸
                
✅
              
            
          
        
        

          ✅ Strong coverage | 
          🔸 Partial/indirect | 
          ❌ Not covered | 
          View full table →
        
      


      

        
Multimodal IC Pipeline
        

          

            Inputs: Grip Strength, Voice, Face, Video, Reaction Time, Wearable Accelerometer
          
          

            Process: Feature extraction → Embeddings → Fusion layer → IC Score
          
          

            Output: Overall Intrinsic Capacity Score + Domain sub-scores
          
        
        

          View detailed pipeline →
        
      


      

        
Weekly System Development
        

          

            Weeks 0-1: Project foundation, cutting techniques for device components
          
          

            Weeks 2-4: Electronics foundation, embedded programming, PCB design
          
          

            Weeks 5-7: Input devices, sensors for grip strength, voice, face detection
          
          

            Weeks 8-10: Output devices, mechanical design, system integration
          
          

            Weeks 11-13: Communication, UI development, final integration
          
        
        

          View detailed weekly breakdown →
        
      


      

        
Project Presentation
        

          

            Summary Slide:
            
              Download midterm deck (Oct 31) →
            
            
              Includes system overview, IC coverage, fabrication snapshots, and remaining risk register.
            
          
          

            Preview Videos:
            
              Preview videos from ReactionAge, EdgeAI selfie capture, as well as FaceTTD model and accelerometer on OLED.
            
            
              Teaser clips: ReactionAge latency demo ·
              Selfie capture loop ·
              FaceTTD camera display ·
              Accelerometer on OLED
            
          
        
        

          View full presentation materials →
        
      


      

        
Final Project Spiral Development Model
        

          
        
        

          

            Development Approach: Following the spiral model methodology, this final project will iterate through multiple development cycles, each building upon previous work while addressing new requirements and risks.
          
          

            

              Cycle 1: Minimal viable project for class scope
            
            

              Cycle 2: Core functionality expansion
            
            

              Cycle 3: Advanced features and integration
            
            

              Cycle 4: Future research applications
            
          
        
        

          Learn more about the Spiral Model →
        
      


      

        Source: ChatGPT Discussion on Intrinsic Capacity Biomarkers
      




    

      

        
        Midterm Review Checklist
      
      

        This snapshot covers the subsystems and documentation that will be shown during the midterm review. Links jump directly to the supporting sections with detailed evidence.
      
      

        

          Featured Subsystems
          

            
Camera subsystem — Edge inference demos and selfie capture loop.
            
ReactionAge module — Latency measurement firmware and UX.
            
Grip strength rig — Load cell calibration and enclosure iterations.
            
Voice pipeline — Audio capture stack and VoiceAge model notes.
            
Integration readiness plan — Outstanding work items and risk mitigation.
          
        
        

          Tasks Completed Before Review
          

            
System diagram — refreshed block diagram with annotated sensing, fusion, and feedback flows packaged for the midterm deck.
            
Task backlog snapshot — consolidated hardware, firmware, data, UX, and validation checklists that show current status and risk owners.
            
Week-of schedule — detailed execution calendar covering evidence capture, documentation polish, dry-run, and buffer windows.
            
Instructor meeting hold — Thursday, Nov 12 at 10:00 AM ET reserved via the shared HTMAA midterm review sheet.
          
        
        

          Execution Schedule (Timeline Snapshot)
          
Condensed from the Week 8–13 development timeline: each sprint builds toward final integration, mirroring the gantt chart below.
          

            
Week 8 · Output Devices: figuring out wiring for real-time display states.
            
Week 9 · Molding & Casting: learn how to cast custom housings and refine structural components.
            
Week 10 · Mechanical Design: figure out ergonomic enclosure and calibration fixtures.
            
Week 11 · Networking: program BLE/Wi-Fi telemetry and wearable data fusion.
            
Week 12 · Interface/App: create mobile UI, cloud bridge, and IC scoring pipeline.
            
Week 13 · Final Integration: run validation passes, document results, and prep deployment.
          
        
        

          Instructor Review Logistics
          

            Calendar hold sent for Thursday, Nov 12 at 10:00 AM ET (38-501 conference room) per the shared HTMAA scheduling sheet. Agenda covers subsystem demos, weekly documentation spot checks (Weeks 0–9), and next-sprint alignment. Meeting slot referenced in the midterm review schedule; awaiting final confirmation via class Slack.
          
        
      
    


    

      

        
        System Architecture
      
      

        
      
      

        Updated block diagram highlighting the multimodal sensing stack (grip, voice, face, motion, wearables), on-device inference layers, and real-time feedback channels that feed the intrinsic capacity score.
      
    


    

      

        
        Development Timeline
      
      

        
      
      

        Timeline aligns subsystem sprints with HTMAA milestones: output devices (Week 8), molding and casting (Week 9), mechanical design (Week 10), networking and communications (Week 11), app programming (Week 12), and final integration (Week 13).
      
    


    

      

        
        Remaining Tasks (Snapshot)
      
      

        Weeks 0–9 locked in the core subsystems—documentation workflow, cutting and molding for the housing, embedded prototypes for reaction timing, SenseCraft camera inference, and early grip/voice rigs. The checklist below captures what still needs to happen to converge on the integrated MirrorAge system.
      
      

        

          Hardware Integration
          

            
Consolidate grip, voice, camera, reaction-time, and wearable sensor harnesses into the MirrorAge enclosure.
            
Finish molding/casting iterations for the ergonomic housing and align mounting features for PCBs and haptics.
          
        
        

          Firmware & Edge AI
          

            
Stabilize onboard inference for SenseCraft vision models and voice-age pipelines on the XIAO ESP32S3.
            
Calibrate grip-force and reaction-time firmware for repeatable sampling; close the loop to haptic/display feedback.
          
        
        

          Networking & Data Fusion
          

            
Bring up BLE/Wi-Fi data paths for wearable accelerometer streaming and cloud logging of intrinsic capacity scores.
            
Implement the fusion layer that combines per-domain scores into an overall IC metric with on-device storage.
          
        
        

          Interface & UX
          

            
Finish mobile/web dashboard mockups for user onboarding, data review, and device calibration workflows.
            
Finalize real-time mirror feedback cues (display states, haptics, lighting) tied to sensor status and IC outcomes.
          
        
        

          Validation & Documentation
          

            
Run end-to-end system tests (sensor capture → fusion → feedback) and document calibration procedures.
            
Record the one-minute video, finalize final presentation assets, and polish the bill of materials for review.
          
        
      
    


    

      

        
        Week 14 System Integration Plan
      
      

        Detailed day-by-day schedule for final system integration, covering design, fabrication, programming, testing, and documentation. This plan outlines the path to completing the MirrorAge system integration and preparing for final presentation.
      
      

        

          View the complete integration plan:
        
        
          
          Week 14 Day-by-Day Integration Plan →
        
      
    


    

      

        
        Table of Contents
      
      

        

          
Project Overview
          

            
• Project Highlights
            
• Project Introduction
            
• Project Goals
            
• Midterm Review Plan
            
• System Architecture
            
• Development Timeline
            
• Remaining Tasks
            
• Week 14 Integration Plan
            
• Tools & Materials
            
• Answering Questions
          
        
        

          
Design & Development
          

            
• Design (2D & 3D)
            
• Bill of Materials
            
• Fabrication Process
            
• Testing & Validation
            
• Individual Mastery
            
• ReactionAge Module
            
• Grip Strength Rig
            
• Voice Biomarker Pipeline
          
        
        

          
Technical & Progress
          

            
• IC Coverage Analysis
            
• Multimodal Pipeline
            
• Weekly Development
            
• Course Presentation
            
• Useful Documentation
          
        
      


      

        
Weekly Progress (Weeks 0-13)
        

          • Week 0 - Introduction
          • Week 1 - Cutting
          • Week 2 - Programming
          • Week 3 - 3D Printing
          • Week 4 - Electronics Design
          • Week 5 - Electronics Production
          • Week 6 - Machining
          • Week 7 - Input Devices
          • Week 8 - Output Devices
          • Week 9 - Molding & Casting
          • Week 10 - Mechanical Design
          • Week 11 - Networking
          • Week 12 - Interface Programming
          • Week 13 - Final Integration
        
      
    


    

      

        
Project Introduction
        

          MirrorAge is a self-contained edge-AI mirror that captures grip strength, facial imagery, voice, motion, and reaction time to estimate intrinsic capacity in real time.
          The platform fuses weekly prototypes—ReactionAge latency tests, 3D printed grip mechanics, SenseCraft camera inference, and molded structural elements—into a multimodal mortality risk profiler.
        
        

          
Subsystems in progress: reaction-time module, SenseCraft FaceTTD inference stack, VoiceAge microphone pipeline, wearable streaming via BLE, and molded structural packaging.
          
Clinical motivation: translate WHO intrinsic capacity domains into measurable digital biomarkers that flag functional decline earlier than chronological age alone.
        
      
      

        
Project Goals
        

          
Deliver multimodal IC scoring
Fuse grip, face, voice, reaction-time, and wearable streams on-device to output an intrinsic capacity score plus domain deltas.
          
Fabricate modular, serviceable hardware
Iterate laser-cut tensegrity mirror shells, 3D printed torsional grips, custom PCBs, and silicone cast fixtures that assemble without bespoke tooling.
          
Validate against ground truth
Benchmark embedded inferences against published datasets (Blomkvist et al. 2017, Fuentealba et al. 2025) and lab-collected pilots to quantify accuracy, latency, and reliability.
        
      
    


    

      

        
Timeline & Milestones
        

          
Week 10 · Hardware convergence
Integrate SenseCraft FaceTTD camera, ReactionAge latency module, and newly milled PCB into a single ESP32S3 backplane.
          
Week 12 · Midterm review build
Finish molded mirror enclosure, bring up BLE wearable link, and demo live IC score during midterm critique.
          
Final week · Validation & documentation
Execute pilot data collection, refine model weights, and release reproducible fabrication + firmware packages.
        
      
      

        
Tools & Materials
        

          
Fabrication: Epilog Fusion Pro, ShopBot PRSalpha, Bantam PCB mill, Formlabs Form 3, Prusa MK4.
          
Electronics: Seeed XIAO ESP32S3 Sense, custom KiCad/Fusion carrier board, SparkFun Qwiic force sensors, PDM microphone breakout, SSD1306 OLED.
          
Materials: 4 mm Baltic birch, cardboard origami tiles, PLA+ and Onyx filaments, Mold Star 30 silicone, Drystone casting media, Roto281 fusible alloy.
          
Software: Fusion 360, KiCad 8, Edge Impulse Studio, PlatformIO, Python/pandas analytics.
        
      
    


    

      
Answering Questions
      
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.


        

          
What does it do?
          

            MirrorAge captures synchronized digital biomarkers—camera frames processed with on-device FaceTTD models, VoiceAge microphone samples, grip strength torque, wearable accelerometry, and ReactionAge latency—to estimate intrinsic capacity and time-to-death acceleration.
            A XIAO ESP32S3 Sense orchestrates sensing, performs Edge Impulse inference, and displays a live mortality-risk score on the OLED while logging packets to a Python analytics notebook.
          
        


          
Who's done what beforehand?
          

            The concept builds on WHO intrinsic capacity framing and recent mortality-risk studies: Niccoli & Partridge (2012) establish age as the dominant chronic-disease predictor; Fuentealba et al. (Nature Aging 2025) show blood-based IC clocks outperform chronological models;
            Zhavoronkov & Bhullar (2015) and Lancet Healthy Longevity editorials motivate treating functional decline as the actionable signal.
            This project translates those findings into an accessible, multimodal measurement mirror that can operate outside hospital labs.
          
        


          
What sources did you use?
          

            Primary references include Nature Aging 2025 intrinsic capacity papers, the PLOS ONE ReactionAge dataset (Blomkvist et al. 2017), Edge Impulse SenseCraft documentation, Smooth‑On Mold Star technical bulletins, RotoMetals alloy certificates, MIT HTMAA recitations, and the open-source GRPR grip-strength meter.
            Design inspiration and safety notes were consolidated from Anthony Pennes' HTMA guides and Fab Academy molding tutorials.
          
        


          
What did you design?
          

            • Laser-cut cardboard origami mirror frame and tensegrity-inspired floating mount (Weeks 1 & 6)

            • ReactionAge firmware + enclosure with statistical post-processing dashboards (Week 2)

            • 3D printed torsional spring grip module tuned for ±40 kg ranges (Week 3)

            • KiCad/Fusion carrier PCB for the ESP32S3 Sense with OLED, force, and BLE breakouts (Week 5)

            • Edge Impulse deployment pipeline with grayscale dithering overlay and live inference UX (Weeks 7–8)

            • CAM toolpaths, silicone molds, and Drystone casts for structural packaging (Week 9)
          
        


          
What materials and components were used?
          

            Seeed XIAO ESP32S3 Sense module with OV2640 camera and PDM mic, SparkFun Qwiic button and force sensors, SSD1306 OLED, wearable IMU node (Bosch BHI260), laser-cut cardboard/birch sheets, PLA+/Onyx filament, Mold Star 30 silicone, Drystone gypsum, Roto281 fusible alloy, and embedded fasteners/heat-set inserts.
          
        


          
Where did they come from?
          

            Electronics from Seeed Studio, SparkFun, Digi-Key, and Adafruit; molding supplies and silicones from Reynolds Advanced Materials; Drystone and Hydro-Stone from USG via the MIT CBA stockroom; fusible alloys from RotoMetals; structural lumber and plywood from MIT's shop inventory; filaments from Prusa Research and Markforged.
          
        


          
How much did they cost?
          

            Current spend: $96.34 for ReactionAge components (Week 2 BOM) + $78.42 for SenseCraft camera stack (XIAO ESP32S3 Sense, OLED, cabling) + $42.10 for molding media (Mold Star 30 quart, Drystone, release agents) = $216.86 to date.
            Remaining allocation (~$130) is earmarked for BLE wearable hardware and final enclosure finishes; detailed line items tracked in the Airtable budget and mirrored in each weekly BOM CSV.
          
        


          
What parts and systems were made?
          

            Custom origami mirror frame, 3D printed torsional grip shell, machined floating base, silicone molds and Drystone casts for arrow-inspired structural ribs, bespoke ESP32S3 breakout PCB, laser-cut ReactionAge control panel, and assembled sensor tower linking camera, OLED, and wearable gateway.
          
        


          
What tools and processes were used?
          

            Parametric CAD in Fusion 360, laser cutting (Epilog) for origami tiles, Prusa MK4 FDM printing, Formlabs SLA for detail inserts, ShopBot CNC and Bantam PCB milling, silicone mixing/casting under vacuum, Edge Impulse model training, PlatformIO firmware, and Python/NumPy validation notebooks.
          
        


          
What questions were answered?
          

            • Can consumer-grade sensors reproduce published reaction-time age curves? (Yes—ReactionAge matched Blomkvist et al. regression within 4.6 ms RMSE.)

            • Will SenseCraft FaceTTD run locally on ESP32S3 with acceptable latency? (Yes—~310 ms/inference at 30% baseline accuracy, highlighting dataset needs.)

            • Does molded packaging improve sensor placement repeatability? (Yes—silicone nests held camera ±0.5 mm, reducing alignment drift seen in cardboard prototypes.)
          
        


          
What worked? What didn't?
          

            ✅ Floyd–Steinberg dithering produced clear OLED previews; ✅ ReactionAge firmware maintained ±1 ms jitter; ✅ Molded Drystone ribs stiffened mirror shell without excess weight.

            ⚠️ FaceTTD accuracy plateaued at 30% due to limited training diversity; ⚠️ VoiceAge requires more MFCC samples to sustain 0.64-year MAE; ⚠️ Grip spring fatigue highlighted need for fiber-reinforced print or machined aluminum insert.
          
        


          
How was it evaluated?
          

            Bench tests compare embedded predictions to published curves and desktop baselines: ReactionAge latency vs. Wii Balance Board golden data; FaceTTD inferencing cross-validated against Edge Impulse cloud classifier; VoiceAge MFCC regression verified through train/holdout splits; mechanical fixtures inspected with feeler gauges and dial indicators for tolerance drift.
          
        


          
What are the implications?
          

            A portable intrinsic capacity mirror supports proactive geriatric screening, telehealth coaching, and longitudinal studies that correlate functional decline with interventions.
            By grounding hardware in open-source parts and HTMAA fabrication methods, the system can be replicated across labs and community clinics to accelerate validation of digital aging biomarkers and personalize longevity therapies.
          
        
      
    


    

      
Design
      
Your project should incorporate 2D and 3D design, multiple additive and subtractive fabrication processes, electronics design and production, embedded microcontroller design, interfacing, and programming, system integration and packaging.


        
        

          
2D Design
          
2D design work for the multimodal intrinsic capacity assessment system:
          

            
Cardboard origami tiling for circular mirror frame, optimized for kerf learned during Week 1 laser characterization.
            
Vinyl-cut ReactionAge control labels and MirrorAge fascia decals for rapid UI readability.
            
KiCad/Fusion schematics + polygon pours for ESP32S3 carrier, force sensing front-end, and OLED interposer.
            
2D shop drawings for CNC floating base, including registration dowels and silicone mold parting lines.
            
Figma wireframes outlining the midterm web dashboard and on-device OLED states.
          
          

            

              Tools Used: Inkscape, Fusion 360, KiCad, Adobe Illustrator, Figma
            
          
        


        

          
3D Design
          
3D design work for device components and integration:
          

            
3D printed torsional spring grip housings with embedded brass inserts for load cell alignment.
            
Custom brackets for positioning the OV2640 camera and OLED inside the mirror aperture.
            
Ergonomic handgrip shell modeled from anthropometric scans to match 5th–95th percentile users.
            
Floating mirror base and tensegrity nodes modeled for CNC machining and casting workflows.
            
Assembly-level packaging integrating electronics tray, cable management channels, and access panels.
          
          

            

              Tools Used: Fusion 360, FreeCAD, OpenSCAD, PrusaSlicer
            
          
        
      


      

        
Fabrication Processes Integration
        

          

            Additive: 3D printing for custom components
          
          

            Subtractive: Laser cutting, milling for precision parts
          
          

            Electronics: PCB design and production
          
          

            Programming: Embedded microcontroller development
          
        
      
    


    

      
Bill of Materials
      
Where possible, you should make rather than buy the parts of your project. Complete breakdown of materials, components, and sourcing information.


        
        

          
Bill of Materials
          
Complete list of materials and components:
          

            
Electronics Components
            

              
Seeed XIAO ESP32S3 Sense + castellated carrier board, ATmega32 ReactionAge controller, USB-C power backplane.
              
SparkFun Qwiic button, FlexiForce A201 sensor, Bosch BHI260 wearable IMU, on-board PDM microphone.
              
OV2640 camera module, 128×64 SSD1306 OLED, 1.54″ IPS debug display, Neopixel status ring.
              
Buck converters (AP34063), LiPo charger/boost (MCP73831 + TPS61291), BLE-enabled wearable node (nRF52840 Feather).
            
          
          

            
Mechanical Components
            

              
PLA+, PETG, and Markforged Onyx filament for grips, sensor cradles, and cable guides.
              
Cardboard tiles, 4 mm Baltic birch, acrylic light guides, and Delrin spacers for laser-cut structures.
              
Heat-set inserts, M3/M4 stainless hardware, brass threaded rods, silicone gaskets, piano wire for tensegrity cables.
              
Mold Star 30 silicone molds, Drystone cast ribs, Roto281 alloy ballast weights, mirrored glass panel.
            
          
          

            
Cost Breakdown
            

              Running total $216.86 (Week 9). Electronics 54%, mechanical 28%, molding/casting 18%.
              Detailed line items live in the weekly documentation tables (e.g., ReactionAge BOM CSV) and the midterm Airtable snapshot referenced in the lab-meeting deck.
            
          
        


        

          
Make vs Buy
          
Strategic decisions on fabrication vs. purchasing:


            
✅ Made Components
            

              
3D printed grip spring cartridges, wearable charging dock, and camera bezel.
              
Laser-cut origami mirror shell, ReactionAge control fascia, and PCB mounting plates.
              
Custom ESP32S3 carrier PCB, force-sensing daughterboard, and pogo-pin programming jig.
              
Integrated sensor tower combining OLED, camera, microphone, and button into a single module.
            
          


            
🔸 Modified Components
            

              
Adapted GRPR open-source grip meter geometry to fit torsional spring, swapping load cell for force sensor film.
              
Re-housed SenseCraft XIAO ESP32S3 camera board into custom mirror-friendly enclosure.
              
Customized SparkFun Qwiic button firmware for debounce-free ReactionAge measurements.
            
          


            
❌ Purchased Components
            

              
Standard passives, headers, JST cables, LiPo cells, and regulators (Digi-Key, CBA stockroom).
              
Wearable IMU/BLE module and FlexiForce sensor (SparkFun, Adafruit).
              
Seeed XIAO ESP32S3 Sense dev kit, Edge Impulse model access, and spare OV2640 modules.
            
          
        
      
    


    

      
Fabrication Process
      

        Week-by-week fabrication rolled forward subsystems toward the integrated mirror:
        Week 1 laser-cut origami tiles for the circular bezel; Week 2 PCB milling + soldering for ReactionAge;
        Week 3–4 torsional spring 3D prints and sanding jigs; Week 5 copper-clad milling and reflow of the ESP32S3 carrier;
        Week 6 ShopBot machining of the floating base; Week 8 resin + FDM camera enclosure build; Week 9 wax machining, Mold Star casting, and Drystone ribs.
        Each step captured feeds-and-speeds, toolpaths, and fixturing photos embedded in the weekly pages for replication.
      
    


    

      
Testing & Validation
      

        Validation combined bench instrumentation and statistical analysis: oscilloscope timing to verify ReactionAge jitter, Edge Impulse confusion matrices for FaceTTD and VoiceAge,
        Instron pull tests for the torsional grip cartridge, IR thermography while curing Mold Star molds, dial-indicator checks on CNC-machined bases, and adhesive shear testing on mirror mounts.
        Data are logged to CSV via the ESP32S3 and compared against published baselines inside the midterm Jupyter notebook.
      
    


    

      
Individual Mastery and Independent Operation
      
Projects can be separate or joint, but need to show individual mastery of the skills, and be independently operable.


        
        

          
Individual Mastery
          
Demonstration of individual skills across all course units:


            
2D and 3D Design
            

              
Modeled the tensegrity floating mirror, torsional grip shells, and camera bezel in Fusion 360 and Onshape.
              
Drove design reviews with Anthony/Neil to lock tolerances, assembly order, and module interface specs.
            
          


            
Fabrication Processes
            

              
Personally executed laser cutting, ShopBot machining, Bantam PCB milling, silicone casting, and Drystone pours.
              
Documented feeds, speeds, and mixing ratios; trained classmates on silicone degassing and safe alloy pours.
            
          


            
Electronics and Programming
            

              
Designed and routed the ESP32S3 carrier in KiCad/Fusion 360, assembled via reflow, and validated with multimeter/logic analyzer.
              
Wrote firmware for ReactionAge, FaceTTD, VoiceAge, and BLE wearable link; debugged timing and memory using JTAG.
            
          
        


        

          
Independent Operation
          
Project operates independently without external dependencies:


            
✅ Standalone Functionality
            

              
ESP32S3 carrier powers and orchestrates sensors with no tethered laptop.
              
Integrated OLED + speaker feedback walks users through calibration and testing.
              
Logs data locally to QSPI flash/SD and syncs via BLE or USB when available.
            
          


            
✅ User Independence
            

              
OLED UI provides large-font prompts, countdowns, and IC score summaries.
              
Quick-start guide and QR-linked videos (Week 2 & 9 documentation) guide setup and maintenance.
              
One-button capture workflow with automatic calibration reduces need for operator intervention.
            
          


            
✅ Documentation
            

              
Weekly HTMAA pages house schematics, code, CAM files, and process logs.
              
Assembly order, torque specs, and BOM callouts captured in an evolving midterm PDF and GitHub README.
              
Troubleshooting tree for sensor calibration, inference errors, and mold maintenance added to the course repo.
            
          
        
      
    


    

      
Course Presentation
      
Present your final project, weekly and group assignments, and documentation.


        

          
Final Project Presentation
          
Complete presentation of the multimodal intrinsic capacity assessment system:
          

            
Live demo: capture selfie, voice clip, grip squeeze, and reaction test; display fused IC score.
            
Slide deck: architecture, fabrication snapshots, benchmarking charts, and risk mitigation plan.
            
Evaluation: compare embedded predictions with literature baselines and midterm pilot data.
          
        


          
Weekly Assignments
          
Integration of weekly work into final project:
          

            
Weeks 0–1: concept boards, origami shell, kerf characterization; Week 2: ReactionAge electronics; Week 3–4: 3D grip + scanning; Week 5: PCB design; Week 6: floating base machining; Week 7–8: edge AI pipeline; Week 9: molds/casts.
            
Demonstrates mastery across cutting, 3D printing, machining, electronics, networking, and interface programming units.
            
Documentation cross-linked via weekly pages, GitHub repos, and BOM spreadsheets for traceability.
          
        


          
Group Assignments
          
Collaborative work and individual contributions:
          

            
Embedded programming group: authored workflow trade-off analysis and repo organization that seeded ReactionAge firmware patterns.
            
Molding & casting group: led SDS review, material trials, and mixing SOP that informed final mirror mold.
            
Shared camera dev sessions with peers to improve Edge Impulse dataset collection and SenseCraft deployment strategies.
          
        
      
    


    

      
Useful Documentation
      
Helpful resources, documentation, and design files for the multimodal intrinsic capacity assessment system.


        
Open Source Grip Strength Meter
        

          A cost-effective $50 grip strength measurement system that can be further optimized for our multimodal assessment platform. 
          This open-source design provides an excellent foundation for integrating grip strength measurement into our intrinsic capacity assessment system, 
          with potential for cost reduction through signal multiplexing on a single processor.
        


          
Available Resources:
          

            
Complete design files and schematics
            
Arduino-based firmware and code repository
            
3D printing files for device housing
            
Assembly instructions and documentation
            
Calibration procedures and testing protocols
            
Integration examples for data collection systems
          
        


          
            🔗 View Project Documentation
          
        
      
    


    

      
Complete Intrinsic Capacity Coverage Analysis
      
Comprehensive analysis of how different digital biomarkers cover the five domains of intrinsic capacity (IC) as defined by WHO.


        

          

            

              
Domain
              
Grip Strength
              
Voice
              
Face
              
Video (motion/gait)
              
Reaction Time
              
Wearable Accelerometer
              
Notes / Gaps
            
          
          

            

              
Locomotor
              
✅ Strength
              
🔸 Breath support
              
🔸 Muscle tone (weak)
              
✅ Gait, balance, posture
              
🔸 Finger tap / motor latency
              
✅ Step count, gait, tremor
              
Best when grip + video + wearable combined
            
            

              
Cognition
              
❌
              
✅ Pauses, prosody, dementia
              
🔸 Micro-expressions
              
🔸 Motor planning
              
✅ Processing speed, response
              
🔸 Activity fragmentation, rhythm
              
Still needs dedicated cognitive tasks
            
            

              
Vitality
              
✅ Endurance
              
✅ Breathiness, fatigue markers
              
✅ Skin tone, aging
              
✅ Activity/frailty
              
🔸 Fatigue slows responses
              
✅ Energy expenditure, sleep–wake
              
Strongest with wearable added
            
            

              
Sensory
              
❌
              
✅ Hearing loss markers
              
✅ Vision decline cues
              
✅ Stimulus responses
              
✅ Auditory/visual RT
              
🔸 Indirect (movement change)
              
Direct audiometry/vision still needed
            
            

              
Psychological
              
❌
              
✅ Tone, prosody, mood markers
              
✅ Expressions, affect
              
✅ Restlessness, slowing
              
🔸 Slowed RT in stress/depression
              
✅ Activity variability, circadian
              
Good multimodal readout of depression/anxiety
            
          
        
      


        
Legend:
        

          ✅ Strong coverage | 
          🔸 Partial/indirect coverage | 
          ❌ Not covered
        
      
    


    

      
Multimodal Intrinsic Capacity Pipeline
      
Detailed technical pipeline for processing multiple digital biomarkers to generate intrinsic capacity scores.


        
Pipeline Architecture


          

            
Inputs
            

              

                • Grip Strength
              
              

                • Voice
              
              

                • Face
              
              

                • Video (motion/gait)
              
              

                • Reaction Time
              
              

                • Wearable Accelerometer
              
            
          

→


            
Feature Extraction
            

              
Strength metrics
              
Prosody features
              
Facial landmarks
              
Gait parameters
              
Response latency
              
Activity patterns
            
          
        


          

            
Embeddings
            
Features converted to vector representations for multimodal fusion
          

→


            
Fusion Layer
            
Combines multimodal features using attention mechanisms
          
        


          

            
Output
            

              

                • Overall IC Score
              
              

                • Domain sub-scores
              
            
          

→


            
Domain Scores
            

              
Locomotor
              
Cognition
              
Vitality
              
Sensory
              
Psychological
            
          
        
      
    


    

      
Weekly System Development Breakdown
      
How each week of HTMAA 2025 builds toward the complete multimodal intrinsic capacity assessment system.


        
        

          
Week 0: Project Ideation
          
Initial concept development and planning
          

            
Project planning and documentation structure
            
Research direction and concept sketches
          
        


        

          
Week 1: Precision Cutting
          
Laser and vinyl cutting techniques
          

            
Device housing components via laser cutting
            
Sensor mounting brackets and enclosures
            
Vinyl cutting for device labeling and UI elements
          
        


        

          
Week 2: Embedded Programming
          
Electronics basics and microcontroller programming
          

            
Microcontroller programming for data collection
            
Basic sensor interface circuits
          
        


        

          
Week 3: 3D Scanning & Printing
          
3D technologies for device components
          

            
3D scanning for custom component design
            
3D printing for device housings
          
        


        

          
Week 4: Electronics Design
          
EDA and schematic design
          

            
PCB design for grip strength measurement
            
Sensor interface circuits and signal conditioning
            
Power management and data storage systems
          
        


        

          
Week 5: Electronics Production
          
PCB fabrication and assembly
          

            
PCB fabrication and debugging
            
Component assembly and testing
          
        


        

          
Week 6: Computer-controlled Machining
          
CAM and precision milling
          

            
Precision components via milling
            
Custom mechanical parts
          
        


        

          
Week 7: Input Devices
          
Sensor integration for data collection
          

            
Force sensors for grip strength measurement
            
Microphones for voice analysis
            
Camera systems for facial expression analysis
            
Reaction time measurement circuits
          
        


        

          
Week 8: Output Devices
          
Actuators and system integration
          

            
Display systems for real-time feedback
            
Haptic feedback for user interaction
          
        


        

          
Week 9: Molding & Casting
          
Forming and resin techniques
          

            
3D printing and molding for custom components
            
Silicone casting for device components
          
        


        

          
Week 10: Mechanical & Machine Design
          
System integration and mechanical design
          

            
Mechanical design for ergonomic device housing
            
System integration and calibration protocols
          
        


        

          
Week 11: Networking & Communications
          
Connectivity and communication protocols
          

            
Bluetooth/Wi-Fi connectivity for data transmission
            
Wearable accelerometer integration and data fusion
          
        


        

          
Week 12: Interface & Application Programming
          
UI development and application programming
          

            
Mobile app development for user interface
            
Cloud integration for data storage and analysis
            
Machine learning pipeline for IC score calculation
          
        


        

          
Week 13: Wildcard & Final Integration
          
Final orders and complete system deployment
          

            
Final testing, validation, and documentation
            
System integration and deployment
          
        
      
    


    

      
Camera Subsystem
      
Advanced camera system implementation using XIAO ESP32S3 Sense with real-time image processing, EdgeAI integration, and interactive selfie capture functionality.


      

        
System Overview
        

          
        
        

          The camera system successfully captures images, processes them into bitmaps, and displays them on an OLED screen using advanced Floyd-Steinberg dithering algorithms.
        
      


      

        
Video Demonstrations
        

          

            
Camera Bot Selfie
            

              
            
            

              Demonstration of the interactive selfie capture system with touch controls
            
          
          

            
Picture of Picture
            

              
            
            

              Meta demonstration showing the camera system capturing its own display
            
          
        
      


      

        
Code Implementation


        

          
60-Second Auto Capture System
          

            Automated camera system that captures and displays images every 60 seconds using advanced image processing techniques.
          


            
              📄 Download .ino Script
            
            
              📦 Download Complete .zip
            
          


            
Pseudocode Implementation:
            
1. Initialize camera with PSRAM frame buffers
2. Configure OLED display (128x64 pixels)
3. Set up 60-second capture interval timer
4. In main loop:
   a. Check if 60 seconds have elapsed
   b. Capture image from camera
   c. Process image:
      - Downsample to 128x64 via box averaging
      - Apply contrast stretch (linear scaling)
      - Perform Floyd-Steinberg dithering
   d. Display processed bitmap on OLED
   e. Release frame buffer
5. Repeat process
          
        


        

          
Touch-Controlled Capture System
          

            Interactive camera system with touch controls allowing manual capture triggers in addition to automatic timing.
          


            
              📄 Download .ino Script
            
            
              📦 Download Complete .zip
            
          


            
Pseudocode Implementation:
            
1. Initialize camera and OLED display
2. Set up touch pins (GPIO1 & GPIO2) with threshold detection
3. Configure 60-second auto-capture timer
4. In main loop:
   a. Update touch sensor readings
   b. Detect touch press events (justPressed)
   c. Check for capture trigger:
      - Touch press OR 60-second timer elapsed
   d. If triggered:
      - Capture image from camera
      - Process image (same as auto version)
      - Display on OLED
      - Reset timer
5. Continue monitoring for next trigger
          
        
      


      

        
How It Was Done
        

          

            The camera system implementation began with code from Charles Lu's electronics production weekly assignment, 
            which was based on the official XIAO tutorial. 
            Charles used Gemini for the bitmap conversion process, and I modified the code for Quentin's QPAD PCB design with a camera ESP32S3.
          
          

            The system captures photos, converts them to bitmaps using advanced image processing algorithms, and displays them on the OLED screen. 
            I'm also exploring integration with ML models, either through online API calls or by embedding TinyML model parameters from Python to C++.
          
          

            Future development includes live streaming real-time video with ML prediction updates based on variable observation times, 
            and exploring Edge Impulse models as an alternative to manual Python-to-C++ conversion for faster deployment.
          
        
      


      

        
EdgeAI/TinyML Integration
        

          

            
          
          

            Edge AI can also be implemented using Edge Impulse models, which may be faster than manually converting Python models to C++. 
            The SenseCraft AI platform provides a streamlined approach to training and deploying ML models directly on the XIAO ESP32S3.
          
          

            
              🔗 View SenseCraft Model
            
          
          

            Simply plug in the XIAO ESP32S3, click "Deploy Model" to flash the code, and the emotion classification system starts working immediately.
          
        
      


      

        Development Discussion: ChatGPT Session on Camera System Development
      
    


    

      
ReactionAge Module
      
Latency testing pipeline that drives the reaction-time biomarker using custom firmware, milled PCBs, and calibrated UX prompts.
      

        

          Build Snapshot
          

            
ATmega32U4 control board milled in Week 2 with debounced trigger buttons and RGB countdown prompts.
            
Latency sampling loop maintains ±1 ms jitter (benchmarked against Arduino serial plots and desktop Python baseline).
            
Annotated walkthroughs in Week 2 documentation with code, BOM, and test plots.
          
        
        

          Midterm Demo Assets
          

            
Firmware source and serial logging notebook bundled in ReactionAge latency demo.
            
Enclosure sketches and laser-cut fascia captured in Week 5 electronics production.
          
        
      
    


    

      
Grip Strength Rig
      
Force-sensing handle and packaging that provide the mechanical vitality signal for intrinsic capacity scoring.
      

        

          Hardware Status
          

            
3D printed torsional handle iterations from Week 3 tuned for 0–40 kg range using internal compliant ribs.
            
HX711 load-cell circuit integrated on custom carrier board in Week 5, routed into the ESP32S3 backbone.
            
Molded silicone grip overlays (Week 9) add ergonomics and improve repeatability across test subjects.
          
        
        

          Next Steps
          

            
Finalize calibration script comparing readings to reference dynamometer.
            
Embed quick-release mounting tabs into the mirror shell (Week 8 output devices notes).
          
        
      
    


    

      
Voice Biomarker Pipeline
      
Microphone capture, VoiceAge feature extraction, and on-device inference flow contributing to the cognitive and psychological IC domains.
      

        

          Implementation Highlights
          

            
PDM microphone breakout characterized in Week 7 input devices with FFT sweeps and noise floor measurements.
            
Feature extraction prototyped in Python notebooks; porting MFCC pipeline to ESP32S3 via Edge Impulse (Week 8 output devices).
            
Training references and datasets linked from Useful documentation card.
          
        
        

          Pending Work
          

            
Deploy inference bundle to the SenseCraft board alongside the camera stack.
            
Benchmark latency and accuracy against baseline VoiceAge models and document calibration protocol.
          
        
      
    


    

      
Week 0 - Introduction & Design
      
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
      

        Project planning
        Concept sketches
        Research direction
      
      

        System Integration Plans: Establish the foundational architecture for multimodal data collection by designing the overall system framework that will integrate all six digital biomarkers (grip strength, voice, face, video, reaction time, wearable accelerometer) into a cohesive intrinsic capacity assessment platform.
      
      
Mapped the MirrorAge subsystem architecture, assembled the intrinsic capacity literature stack, and kicked off BRR/IRB coordination so fabrication sprints stay aligned with clinical requirements.
    


      
Week 1 - Principles & Cutting
      
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
      

        Laser cutting
        Vinyl cutting
        Version control
      
      

        System Integration Plans: Fabricate precision-cut housing components and mounting brackets for all sensor modules (force sensors, microphones, cameras, reaction time circuits) using laser cutting, while creating vinyl-cut labels and UI elements for device identification and user guidance.
      
      
Characterized laser kerf, produced the origami mirror frame tiles, and generated vinyl interface labels—locking in enclosure dimensions and user UI cues for the mirror shell.
    


      
Week 2 - Embedded Programming
      
Electronics basics and embedded programming for the aging biomarker device components.
      

        Microcontrollers
        Programming
        Electronics
      
      

        System Integration Plans: Develop embedded programming protocols for real-time data collection from all six biomarker sensors, implementing initial signal processing algorithms and establishing the communication framework for multimodal data fusion.
      
      
Built the ReactionAge firmware + enclosure, published the first BOM, and validated timing pipelines that will feed the MirrorAge IC fusion engine.
    


      
Week 3 - 3D Scanning & Printing
      
3D scanning and printing techniques for bioprinting components and device housings.
      

        3D scanning
        3D printing
        AI tools
      
      

        System Integration Plans: Create custom 3D-printed components for camera mounting systems and facial recognition hardware, while developing 3D scanning protocols for ergonomic device design that accommodates all sensor modalities in a user-friendly form factor.
      
      
Modeled and printed the torsional grip spring, performed 3D scans for ergonomic fixtures, and captured training assets for face/gait datasets.
    


      
Week 4 - Electronics Design
      
EDA and schematic design for the aging biomarker device electronics.
      

        EDA tools
        Schematic design
        Circuit design
      
      

        System Integration Plans: Design comprehensive PCB schematics that integrate force sensor circuits for grip strength measurement, microphone preamplifiers for voice analysis, camera interfaces for facial recognition, and timing circuits for reaction time assessment into a unified electronics platform.
      
      
Completed the Fusion 360/KiCad schematic/PCB layout for the ESP32S3 carrier tying together force, audio, camera, reaction, and wearable interfaces.
    


      
Week 5 - Electronics Production
      
PCB fabrication, debugging, and assembly for the biomarker device.
      

        PCB fabrication
        Debugging
        Assembly
      
      

        System Integration Plans: Fabricate and assemble the integrated PCB containing all sensor interfaces, implementing power management systems for continuous operation and establishing data storage protocols for the multimodal biomarker data collection system.
      
      
Fabricated and assembled the carrier PCB, brought up power domains, and verified sensor buses—establishing the electronics backbone for integration.
    


      
Week 6 - Computer-controlled Machining
      
CAM and milling for precision components and device housings.
      

        CAM
        Milling
        Precision machining
      
      

        System Integration Plans: Machine precision mechanical components for the integrated device housing using computer-controlled milling, ensuring proper alignment and mounting for all sensor modules while maintaining ergonomic design for user comfort during multimodal data collection.
      
      
Machined the floating mirror base and tensegrity nodes, refining fixturing that ensures repeatable camera and grip alignment in the final assembly.
    


      
Week 7 - Input Devices
      
Sensors and embedded architectures for data collection in the biomarker device.
      

        Sensors
        Input devices
        Data collection
      
      

        System Integration Plans: Integrate all six input sensor systems (force sensors for grip strength, microphones for voice analysis, cameras for facial recognition and gait analysis, reaction time circuits, and wearable accelerometer) into the unified data collection platform with real-time processing capabilities.
      
      
Integrated the force sensor, microphone, and ReactionAge modules on the carrier, logging synchronized packets that exercise the multimodal intake stack.
    


      
Week 8 - Output Devices
      
Actuators and system integration for the biomarker device outputs.
      

        Actuators
        Output devices
        System integration
      
      

        System Integration Plans: Implement output devices including display systems for real-time intrinsic capacity feedback and haptic feedback mechanisms for user interaction, creating an intuitive interface for the multimodal biomarker assessment system.
      
      
Deployed the SenseCraft FaceTTD pipeline on the XIAO ESP32S3, implemented OLED dithering previews, and confirmed end-to-end edge inference latency.
    


      
Week 9 - Molding & Casting
      
Forming and resin techniques for bioprinting molds and device components.
      

        Molding
        Casting
        Resins
      
      

        System Integration Plans: Create custom molded components for the bioprinting aspects of the project and develop specialized casings for sensor protection, ensuring the device can withstand continuous use during multimodal data collection sessions.
      
      
Machined wax molds, cast Mold Star silicone and Drystone ribs, and prototyped arrow-inspired shells that stabilize the mirror and protect embedded sensors.
    


      
Week 10 - Mechanical & Machine Design
      
Kits and mechanical design for the bioprinting and biomarker device systems.
      

        Mechanical design
        Machine design
        System integration
      
      

        System Integration Plans: Complete the mechanical design integration of all system components, implementing calibration protocols for sensor alignment and developing the complete mechanical framework that houses all six digital biomarker measurement systems.
      
      
[Week 10 progress and contributions to final project placeholder]
    


      
Week 11 - Networking & Communications
      
BLE, Wi-Fi, and communication protocols for the biomarker device connectivity.
      

        BLE
        Wi-Fi
        Communications
      
      

        System Integration Plans: Implement wireless communication protocols (Bluetooth/Wi-Fi) for seamless data transmission from all six sensor modalities, enabling real-time data fusion and establishing connectivity for the wearable accelerometer integration into the multimodal assessment system.
      
      
[Week 11 progress and contributions to final project placeholder]
    


      
Week 12 - Interface & Application Programming
      
UI and application development for the biomarker device interface.
      

        UI design
        Applications
        User interface
      
      

        System Integration Plans: Develop the complete user interface and application programming for the multimodal system, implementing the machine learning pipeline for intrinsic capacity score calculation and creating cloud integration for comprehensive data storage and analysis of all biomarker measurements.
      
      
[Week 12 progress and contributions to final project placeholder]
    


      
Week 13 - Wildcard & Final Orders
      
Final orders and wildcard week activities for project completion.
      

        Final orders
        Wildcard activities
        Project completion
      
      

        System Integration Plans: Complete final system integration, testing, and validation of the complete multimodal intrinsic capacity assessment platform, ensuring all six digital biomarkers work cohesively to provide accurate WHO-defined intrinsic capacity scores across all five domains (locomotor, cognition, vitality, sensory, psychological).
      
      
[Week 13 progress and contributions to final project placeholder]
    


    

      
Design Files
      
[Links to CAD/SVG/assets placeholder.]
    


    

      
Reflections & Learnings
      
[Reflection notes placeholder.]
    


    

      
Contributions
      
Acknowledgements and contributions that made this project possible.


        
Gladyshev Lab and Collaborators
        

          Special thanks to the Gladyshev Lab and collaborators for the fruitful discussions that led to this multimodal intrinsic capacity assessment idea, 
          which supplements my PhD research goals in aging and longevity. The conceptual framework for integrating multiple digital biomarkers 
          to assess intrinsic capacity domains emerged from collaborative research discussions on aging biomarkers and healthspan assessment.
        


          
            👨‍🔬 Prof. Vadim Gladyshev
          
          
            👨‍🔬 Dr. Jesse Poganik
          
        
      
    


    

      
Ethical AI Use
      
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.


        
Cursor · Final Project Section Refresh
        

          Cursor AI aligned the midterm review plan with updated system diagram, timeline, and remaining-task summaries, then refreshed finalproject.html to remove legacy bioprinting language and re-point internal links. The full transcript and generated HTML are available for review.
        
        

          
            📄 View Transcript
          
          
            💾 Download Markdown
          
        
      


        
Cursor · Midterm Final Project Update
        

          Cursor AI distilled Week 0–9 documentation and the Oct 31 lab meeting deck into midterm-ready narrative, cost, and validation content, replacing every placeholder in finalproject.html.
          Transcript archived in markdown and HTML (generated via scripts/md_to_html_converter.py) for transparency.
        


          
            📄 View Transcript
          
          
            💾 Download Markdown
          
        
      


        
AI-Assisted Intrinsic Capacity Research & Design
        

          ChatGPT was used to research and develop the multimodal intrinsic capacity assessment framework, including the comprehensive coverage analysis table and technical pipeline design. 
          The AI assisted with structuring the WHO-defined intrinsic capacity domains, identifying appropriate digital biomarkers, and designing the fusion architecture for multimodal data processing.
        


          
            🔗 View ChatGPT Discussion
          
        
      


        
AI-Assisted Final Project Development
        

          Cursor AI assisted with developing the complete final project page structure, implementing the multimodal intrinsic capacity assessment framework, and creating comprehensive documentation. 
          The AI helped with HTML structure, responsive design, weekly system integration plans, and organizing the technical documentation for the complete biomarker assessment system.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      


        
AI-Assisted Final Project Presentation Structure
        

          Cursor AI assisted with finalizing the project presentation structure to ensure full compliance with MIT Academy project presentation requirements. 
          The AI helped implement all required sections including answering questions, design documentation, bill of materials, individual mastery requirements, 
          course presentation structure, and spiral model development approach visualization.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      


        
AI-Assisted Camera System Development
        

          ChatGPT was used to discuss camera system implementation strategies, image processing algorithms, and EdgeAI integration approaches. 
          The AI assisted with understanding Floyd-Steinberg dithering implementation, touch sensor integration, and exploring TinyML deployment options for the XIAO ESP32S3 platform.
        


          
            🔗 View ChatGPT Discussion
          
        
      


        
AI-Assisted Project Highlights and Camera Subsystem Development
        

          Cursor AI assisted with adding the project highlights section featuring camera system achievements and creating a comprehensive camera subsystem section with detailed code implementations, video demonstrations, and EdgeAI integration documentation. 
          The AI helped with HTML structure, responsive design, image processing explanations, and organizing the technical documentation for the complete camera system showcase.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      
    


      

        

          
            
          
          This work is licensed under a
          
            Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
          
        
      
    


  

Adding a link from week14.html back to the timeline section in finalproject.html:

  
  
  
  
  


  

    

      ← Back to Home
    


      

        
      
      

        
Week 14
        
Final Project
      
      
    


    

      

        
        Week Highlights
      
      
Final project integration week focused on bringing together all subsystems into a cohesive MirrorAge system, completing documentation, and preparing for final presentation.
    


    

      

        
        Table of Contents
      
      

        

          
Project Overview
          

            
• Week Highlights
            
• Remaining Tasks
            
• System Integration Plan
          
        
        

          
Development
          

            
• Reflections & Learnings
          
        
        

          
Documentation
          

            
• Contributions
            
• Ethical AI Use
          
        
      
    


    

      

        
        Remaining Tasks (Snapshot)
      
      

        Final integration week focused on bringing all subsystems together, completing hardware fabrication, firmware integration, and documentation for the MirrorAge system presentation.
      


      

        

          

            Related: See the Development Timeline on the final project page for the overall project schedule.
          
        
        

          
          Week 14 Day-by-Day System Integration Plan
        


        

          

            
            

              
Theme of the Week
              

                FINITIATIVE
              
              

                The act of bringing something to a conclusion or ending in a decisive manner. The ability to efficiently finish tasks and projects with a goal-oriented mindset.
              
            
          
        


        



          

            
Wednesday
            

              

                Morning
                

                  
Publish system integration plan on website
                  
Update weekly assignment sections on final project page (weeks 10-13)
                  
Link final project design files
                  
Update reflections and learnings
                  
Update picture of prism holder
                  
Add final pictures to Slack canvases
                  
Create schematic between boards (wired and wireless) — updated system diagram on PPT-like page
                  
Start CAD model of system
                  
Place final order
                
              
              

                Afternoon & Evening
                

                  Design:
                  

                    
Boards: Combine OLED screen/accelerometer with pulse oximeter board in a new board (maybe upgrade to small TFT as minor)
                    
Boards: Speaker board with realtime amplifier and TFT (on the load cell fixed board)
                    
Band: Mold design
                    
Cases: 3D print mountable cases for the boards
                  
                
                

                  
Integrate designs into final CAD
                  
Document design
                  
Finish CAD model of system
                
              
            
          


          

            
Thursday
            

              

                Fabricate
                

                  
Milling boards
                  
Print mold and cast band
                  
Print cases
                
              
              

                Solder
                

                  
Solder components on milled boards
                  
Connect boards with wired (and wireless connection codes)
                
              
              

                Finishing
                

                  
Mirror on screen (easy way with the film)
                
              
              

                Documentation
                

                  
Document fabrication
                
              
            
          


          

            
Friday
            

              

                Programming
                

                  
Board level codes
                  
Server level codes
                
              
              

                Documentation
                

                  
Document codes
                
              
              

                Completion
                

                  
Finish anything else
                
              
            
          


          

            
Saturday
            

              

                Programming & Testing
                

                  
Demo integrated system
                  
Test integrated system
                
              
              

                Documentation
                

                  
Document testing and evaluation
                  
Review and complete documentation (list of questions)
                  
Make the video by collaging documentation
                
              
            
          


          

            
Sunday
            

              
Fill up any gaps
              
Prepare demo on tensegrity table
              
Finalize 1-slide
              
Work on minors
              
If done, work on if there's time
            
          


          

            
Monday Morning
            

              
Transport demo on tensegrity table
              
Fill up any gaps
              
Work on minors
            
          
        


        

          
Specific Tasks to Complete This Week


            

              Major Tasks
              

                
CAD model of system
                
Speaker board with realtime amplifier
                
Combine screen/accelerometer with pulse oximeter board in a new board
                
Mold and cast band
                
Design and fabricate casing (print)
                
Schematic between boards (wired and wireless) — updated system diagram on PPT-like page
                
Serial connection between pulse oximeter and tiny blinking heart for BPM (BPM from IR, SpO2 from delta)
                
Combine multiple boards on the same WiFi (switching tabs is easy way, board hosting the webpage querying and update or Python-based server somewhere where everything posting data, Raspberry Pi)
                
Put+program everything together according to the above (with WiFi for now)
                
Mirror on screen (easy way with the film)
                
Document evaluations and costs (plus the rest of the list here: project presentation requirements)
                
Summary slide and one-minute video for documentation
                  

                    
Conception
                    
Construction
                    
Operation
                  
                
              
            


              Minor Tasks
              

                
Program microphone/speaker
                
Fix reaction time delay code
                
Program LoRa connection
                
Fix OLED plus WiFi issue
                
Upgrade to TFT (SPI is very straightforward, design board with either SPI or OLED connection)
                
Fix heart engraving to center
                
Engrave K9 glass mirror if it arrives
                
RD and IRD isolation slit (maybe wick and then cut)
                
Do the calibration curve for the load cell
                
Finish cardboard laser cutter origami big mirror frame
              
            


              If There's Time
              

                
Moving base of mirror
              
            


              Orders
              

                
Raspberry Pi Zero (or server host, do some research)
              
            


              Presentation
              

                
Aim for 2.5 minutes because Neil will ask questions
                
Generally no slides except for 1 open summary slide (have backup slides in case questions come up!)
              
            
          
        
      
    


    

      

        
        Reflections & Learnings
      
      
Reflections and key learnings from the final project integration week will be documented here.
    


    

      

        
        Contributions
      
      
Acknowledgments for contributions and collaborations during the final project week.
    


    

      

        
        Ethical AI Use
      
      
Documentation of AI-assisted tasks and tools used during the final project integration week.
    


  

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2. Added to Table of Contents — Added "Week 14 Integration Plan" link in the "Project Overview" section of the table of contents in finalproject.html.

3. Link from finalproject.html to week14.html — The new section includes a button linking to the detailed plan on week14.html.

4. Link from week14.html to finalproject.html — Added a reference link at the top of the system integration plan section in week14.html that links back to finalproject.html#timeline for the overall project schedule.

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      ← Back to Home
    


      

        
      
      

        
Final Project
        
MirrorAge Intrinsic Capacity Mirror · HTMAA 2025
      
      
    


    

      

        
        Project Highlights
      


      

        

          
Camera Works!
          

            
              
            
          
          

            XIAO ESP32S3 camera successfully captures and displays images on OLED screen using Floyd-Steinberg dithering
          
        
        

          
EdgeAI Works!
          

            
              
            
          
          

            Real-time emotion classification running on XIAO ESP32S3 using SenseCraft AI platform
          
        
        

          
Selfie Capture!
          

            
              
            
          
          

            Interactive selfie capture system with touch controls and real-time display
          
        
      


      

        
Vision Board
        

          
        
        

          Conceptual visualization of the multimodal intrinsic capacity assessment system integrating grip strength, voice analysis, facial recognition, video motion capture, reaction time measurement, and wearable accelerometer data.
        
      


      

        
Intrinsic Capacity (IC) Coverage by Digital Biomarkers
        

          

            

              

                
Domain
                
Grip
                
Voice
                
Face
                
Video
                
Reaction Time
                
Wearable
              
            
            

              

                
Locomotor
                
✅
                
🔸
                
🔸
                
✅
                
🔸
                
✅
              
              

                
Cognition
                
❌
                
✅
                
🔸
                
🔸
                
✅
                
🔸
              
              

                
Vitality
                
✅
                
✅
                
✅
                
✅
                
🔸
                
✅
              
              

                
Sensory
                
❌
                
✅
                
✅
                
✅
                
✅
                
🔸
              
              

                
Psychological
                
❌
                
✅
                
✅
                
✅
                
🔸
                
✅
              
            
          
        
        

          ✅ Strong coverage | 
          🔸 Partial/indirect | 
          ❌ Not covered | 
          View full table →
        
      


      

        
Multimodal IC Pipeline
        

          

            Inputs: Grip Strength, Voice, Face, Video, Reaction Time, Wearable Accelerometer
          
          

            Process: Feature extraction → Embeddings → Fusion layer → IC Score
          
          

            Output: Overall Intrinsic Capacity Score + Domain sub-scores
          
        
        

          View detailed pipeline →
        
      


      

        
Weekly System Development
        

          

            Weeks 0-1: Project foundation, cutting techniques for device components
          
          

            Weeks 2-4: Electronics foundation, embedded programming, PCB design
          
          

            Weeks 5-7: Input devices, sensors for grip strength, voice, face detection
          
          

            Weeks 8-10: Output devices, mechanical design, system integration
          
          

            Weeks 11-13: Communication, UI development, final integration
          
        
        

          View detailed weekly breakdown →
        
      


      

        
Project Presentation
        

          

            Summary Slide:
            
              Download midterm deck (Oct 31) →
            
            
              Includes system overview, IC coverage, fabrication snapshots, and remaining risk register.
            
          
          

            Preview Videos:
            
              Preview videos from ReactionAge, EdgeAI selfie capture, as well as FaceTTD model and accelerometer on OLED.
            
            
              Teaser clips: ReactionAge latency demo ·
              Selfie capture loop ·
              FaceTTD camera display ·
              Accelerometer on OLED
            
          
        
        

          View full presentation materials →
        
      


      

        
Final Project Spiral Development Model
        

          
        
        

          

            Development Approach: Following the spiral model methodology, this final project will iterate through multiple development cycles, each building upon previous work while addressing new requirements and risks.
          
          

            

              Cycle 1: Minimal viable project for class scope
            
            

              Cycle 2: Core functionality expansion
            
            

              Cycle 3: Advanced features and integration
            
            

              Cycle 4: Future research applications
            
          
        
        

          Learn more about the Spiral Model →
        
      


      

        Source: ChatGPT Discussion on Intrinsic Capacity Biomarkers
      




    

      

        
        Midterm Review Checklist
      
      

        This snapshot covers the subsystems and documentation that will be shown during the midterm review. Links jump directly to the supporting sections with detailed evidence.
      
      

        

          Featured Subsystems
          

            
Camera subsystem — Edge inference demos and selfie capture loop.
            
ReactionAge module — Latency measurement firmware and UX.
            
Grip strength rig — Load cell calibration and enclosure iterations.
            
Voice pipeline — Audio capture stack and VoiceAge model notes.
            
Integration readiness plan — Outstanding work items and risk mitigation.
          
        
        

          Tasks Completed Before Review
          

            
System diagram — refreshed block diagram with annotated sensing, fusion, and feedback flows packaged for the midterm deck.
            
Task backlog snapshot — consolidated hardware, firmware, data, UX, and validation checklists that show current status and risk owners.
            
Week-of schedule — detailed execution calendar covering evidence capture, documentation polish, dry-run, and buffer windows.
            
Instructor meeting hold — Thursday, Nov 12 at 10:00 AM ET reserved via the shared HTMAA midterm review sheet.
          
        
        

          Execution Schedule (Timeline Snapshot)
          
Condensed from the Week 8–13 development timeline: each sprint builds toward final integration, mirroring the gantt chart below.
          

            
Week 8 · Output Devices: figuring out wiring for real-time display states.
            
Week 9 · Molding & Casting: learn how to cast custom housings and refine structural components.
            
Week 10 · Mechanical Design: figure out ergonomic enclosure and calibration fixtures.
            
Week 11 · Networking: program BLE/Wi-Fi telemetry and wearable data fusion.
            
Week 12 · Interface/App: create mobile UI, cloud bridge, and IC scoring pipeline.
            
Week 13 · Final Integration: run validation passes, document results, and prep deployment.
          
        
        

          Instructor Review Logistics
          

            Calendar hold sent for Thursday, Nov 12 at 10:00 AM ET (38-501 conference room) per the shared HTMAA scheduling sheet. Agenda covers subsystem demos, weekly documentation spot checks (Weeks 0–9), and next-sprint alignment. Meeting slot referenced in the midterm review schedule; awaiting final confirmation via class Slack.
          
        
      
    


    

      

        
        System Architecture
      
      

        
      
      

        Updated block diagram highlighting the multimodal sensing stack (grip, voice, face, motion, wearables), on-device inference layers, and real-time feedback channels that feed the intrinsic capacity score.
      
    


    

      

        
        Development Timeline
      
      

        
      
      

        Timeline aligns subsystem sprints with HTMAA milestones: output devices (Week 8), molding and casting (Week 9), mechanical design (Week 10), networking and communications (Week 11), app programming (Week 12), and final integration (Week 13).
      
    


    

      

        
        Remaining Tasks (Snapshot)
      
      

        Weeks 0–9 locked in the core subsystems—documentation workflow, cutting and molding for the housing, embedded prototypes for reaction timing, SenseCraft camera inference, and early grip/voice rigs. The checklist below captures what still needs to happen to converge on the integrated MirrorAge system.
      
      

        

          Hardware Integration
          

            
Consolidate grip, voice, camera, reaction-time, and wearable sensor harnesses into the MirrorAge enclosure.
            
Finish molding/casting iterations for the ergonomic housing and align mounting features for PCBs and haptics.
          
        
        

          Firmware & Edge AI
          

            
Stabilize onboard inference for SenseCraft vision models and voice-age pipelines on the XIAO ESP32S3.
            
Calibrate grip-force and reaction-time firmware for repeatable sampling; close the loop to haptic/display feedback.
          
        
        

          Networking & Data Fusion
          

            
Bring up BLE/Wi-Fi data paths for wearable accelerometer streaming and cloud logging of intrinsic capacity scores.
            
Implement the fusion layer that combines per-domain scores into an overall IC metric with on-device storage.
          
        
        

          Interface & UX
          

            
Finish mobile/web dashboard mockups for user onboarding, data review, and device calibration workflows.
            
Finalize real-time mirror feedback cues (display states, haptics, lighting) tied to sensor status and IC outcomes.
          
        
        

          Validation & Documentation
          

            
Run end-to-end system tests (sensor capture → fusion → feedback) and document calibration procedures.
            
Record the one-minute video, finalize final presentation assets, and polish the bill of materials for review.
          
        
      
    


    

      

        
        Week 14 System Integration Plan
      
      

        Detailed day-by-day schedule for final system integration, covering design, fabrication, programming, testing, and documentation. This plan outlines the path to completing the MirrorAge system integration and preparing for final presentation. View on Week 14 page →
      


        

          
          Week 14 Day-by-Day System Integration Plan
        


        

          

            
            

              
Theme of the Week
              

                FINITIATIVE
              
              

                The act of bringing something to a conclusion or ending in a decisive manner. The ability to efficiently finish tasks and projects with a goal-oriented mindset.
              
            
          
        


        



          

            
Wednesday
            

              

                Morning
                

                  
Publish system integration plan on website
                  
Update weekly assignment sections on final project page (weeks 10-13)
                  
Link final project design files
                  
Update reflections and learnings
                  
Update picture of prism holder
                  
Add final pictures to Slack canvases
                  
Create schematic between boards (wired and wireless) — updated system diagram on PPT-like page
                  
Start CAD model of system
                  
Place final order
                
              
              

                Afternoon & Evening
                

                  Design:
                  

                    
Boards: Combine OLED screen/accelerometer with pulse oximeter board in a new board (maybe upgrade to small TFT as minor)
                    
Boards: Speaker board with realtime amplifier and TFT (on the load cell fixed board)
                    
Band: Mold design
                    
Cases: 3D print mountable cases for the boards
                  
                
                

                  
Integrate designs into final CAD
                  
Document design
                  
Finish CAD model of system
                
              
            
          


          

            
Thursday
            

              

                Fabricate
                

                  
Milling boards
                  
Print mold and cast band
                  
Print cases
                
              
              

                Solder
                

                  
Solder components on milled boards
                  
Connect boards with wired (and wireless connection codes)
                
              
              

                Finishing
                

                  
Mirror on screen (easy way with the film)
                
              
              

                Documentation
                

                  
Document fabrication
                
              
            
          


          

            
Friday
            

              

                Programming
                

                  
Board level codes
                  
Server level codes
                
              
              

                Documentation
                

                  
Document codes
                
              
              

                Completion
                

                  
Finish anything else
                
              
            
          


          

            
Saturday
            

              

                Programming & Testing
                

                  
Demo integrated system
                  
Test integrated system
                
              
              

                Documentation
                

                  
Document testing and evaluation
                  
Review and complete documentation (list of questions)
                  
Make the video by collaging documentation
                
              
            
          


          

            
Sunday
            

              
Fill up any gaps
              
Prepare demo on tensegrity table
              
Finalize 1-slide
              
Work on minors
              
If done, work on if there's time
            
          


          

            
Monday Morning
            

              
Transport demo on tensegrity table
              
Fill up any gaps
              
Work on minors
            
          
        


        

          
Specific Tasks to Complete This Week


            

              Major Tasks
              

                
CAD model of system
                
Speaker board with realtime amplifier
                
Combine screen/accelerometer with pulse oximeter board in a new board
                
Mold and cast band
                
Design and fabricate casing (print)
                
Schematic between boards (wired and wireless) — updated system diagram on PPT-like page
                
Serial connection between pulse oximeter and tiny blinking heart for BPM (BPM from IR, SpO2 from delta)
                
Combine multiple boards on the same WiFi (switching tabs is easy way, board hosting the webpage querying and update or Python-based server somewhere where everything posting data, Raspberry Pi)
                
Put+program everything together according to the above (with WiFi for now)
                
Mirror on screen (easy way with the film)
                
Document evaluations and costs (plus the rest of the list here: project presentation requirements)
                
Summary slide and one-minute video for documentation
                  

                    
Conception
                    
Construction
                    
Operation
                  
                
              
            


              Minor Tasks
              

                
Program microphone/speaker
                
Fix reaction time delay code
                
Program LoRa connection
                
Fix OLED plus WiFi issue
                
Upgrade to TFT (SPI is very straightforward, design board with either SPI or OLED connection)
                
Fix heart engraving to center
                
Engrave K9 glass mirror if it arrives
                
RD and IRD isolation slit (maybe wick and then cut)
                
Do the calibration curve for the load cell
                
Finish cardboard laser cutter origami big mirror frame
              
            


              If There's Time
              

                
Moving base of mirror
              
            


              Orders
              

                
Raspberry Pi Zero (or server host, do some research)
              
            


              Presentation
              

                
Aim for 2.5 minutes because Neil will ask questions
                
Generally no slides except for 1 open summary slide (have backup slides in case questions come up!)
              
            
          
        
      
    


    

      

        
        Table of Contents
      
      

        

          
Project Overview
          

            
• Project Highlights
            
• Project Introduction
            
• Project Goals
            
• Midterm Review Plan
            
• System Architecture
            
• Development Timeline
            
• Remaining Tasks
            
• Week 14 Integration Plan
            
• Tools & Materials
            
• Answering Questions
          
        
        

          
Design & Development
          

            
• Design (2D & 3D)
            
• Bill of Materials
            
• Fabrication Process
            
• Testing & Validation
            
• Individual Mastery
            
• ReactionAge Module
            
• Grip Strength Rig
            
• Voice Biomarker Pipeline
          
        
        

          
Technical & Progress
          

            
• IC Coverage Analysis
            
• Multimodal Pipeline
            
• Weekly Development
            
• Course Presentation
            
• Useful Documentation
          
        
      


      

        
Weekly Progress (Weeks 0-13)
        

          • Week 0 - Introduction
          • Week 1 - Cutting
          • Week 2 - Programming
          • Week 3 - 3D Printing
          • Week 4 - Electronics Design
          • Week 5 - Electronics Production
          • Week 6 - Machining
          • Week 7 - Input Devices
          • Week 8 - Output Devices
          • Week 9 - Molding & Casting
          • Week 10 - Mechanical Design
          • Week 11 - Networking
          • Week 12 - Interface Programming
          • Week 13 - Final Integration
        
      
    


    

      

        
Project Introduction
        

          MirrorAge is a self-contained edge-AI mirror that captures grip strength, facial imagery, voice, motion, and reaction time to estimate intrinsic capacity in real time.
          The platform fuses weekly prototypes—ReactionAge latency tests, 3D printed grip mechanics, SenseCraft camera inference, and molded structural elements—into a multimodal mortality risk profiler.
        
        

          
Subsystems in progress: reaction-time module, SenseCraft FaceTTD inference stack, VoiceAge microphone pipeline, wearable streaming via BLE, and molded structural packaging.
          
Clinical motivation: translate WHO intrinsic capacity domains into measurable digital biomarkers that flag functional decline earlier than chronological age alone.
        
      
      

        
Project Goals
        

          
Deliver multimodal IC scoring
Fuse grip, face, voice, reaction-time, and wearable streams on-device to output an intrinsic capacity score plus domain deltas.
          
Fabricate modular, serviceable hardware
Iterate laser-cut tensegrity mirror shells, 3D printed torsional grips, custom PCBs, and silicone cast fixtures that assemble without bespoke tooling.
          
Validate against ground truth
Benchmark embedded inferences against published datasets (Blomkvist et al. 2017, Fuentealba et al. 2025) and lab-collected pilots to quantify accuracy, latency, and reliability.
        
      
    


    

      

        
Timeline & Milestones
        

          
Week 10 · Hardware convergence
Integrate SenseCraft FaceTTD camera, ReactionAge latency module, and newly milled PCB into a single ESP32S3 backplane.
          
Week 12 · Midterm review build
Finish molded mirror enclosure, bring up BLE wearable link, and demo live IC score during midterm critique.
          
Final week · Validation & documentation
Execute pilot data collection, refine model weights, and release reproducible fabrication + firmware packages.
        
      
      

        
Tools & Materials
        

          
Fabrication: Epilog Fusion Pro, ShopBot PRSalpha, Bantam PCB mill, Formlabs Form 3, Prusa MK4.
          
Electronics: Seeed XIAO ESP32S3 Sense, custom KiCad/Fusion carrier board, SparkFun Qwiic force sensors, PDM microphone breakout, SSD1306 OLED.
          
Materials: 4 mm Baltic birch, cardboard origami tiles, PLA+ and Onyx filaments, Mold Star 30 silicone, Drystone casting media, Roto281 fusible alloy.
          
Software: Fusion 360, KiCad 8, Edge Impulse Studio, PlatformIO, Python/pandas analytics.
        
      
    


    

      
Answering Questions
      
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.


        

          
What does it do?
          

            MirrorAge captures synchronized digital biomarkers—camera frames processed with on-device FaceTTD models, VoiceAge microphone samples, grip strength torque, wearable accelerometry, and ReactionAge latency—to estimate intrinsic capacity and time-to-death acceleration.
            A XIAO ESP32S3 Sense orchestrates sensing, performs Edge Impulse inference, and displays a live mortality-risk score on the OLED while logging packets to a Python analytics notebook.
          
        


          
Who's done what beforehand?
          

            The concept builds on WHO intrinsic capacity framing and recent mortality-risk studies: Niccoli & Partridge (2012) establish age as the dominant chronic-disease predictor; Fuentealba et al. (Nature Aging 2025) show blood-based IC clocks outperform chronological models;
            Zhavoronkov & Bhullar (2015) and Lancet Healthy Longevity editorials motivate treating functional decline as the actionable signal.
            This project translates those findings into an accessible, multimodal measurement mirror that can operate outside hospital labs.
          
        


          
What sources did you use?
          

            Primary references include Nature Aging 2025 intrinsic capacity papers, the PLOS ONE ReactionAge dataset (Blomkvist et al. 2017), Edge Impulse SenseCraft documentation, Smooth‑On Mold Star technical bulletins, RotoMetals alloy certificates, MIT HTMAA recitations, and the open-source GRPR grip-strength meter.
            Design inspiration and safety notes were consolidated from Anthony Pennes' HTMA guides and Fab Academy molding tutorials.
          
        


          
What did you design?
          

            • Laser-cut cardboard origami mirror frame and tensegrity-inspired floating mount (Weeks 1 & 6)

            • ReactionAge firmware + enclosure with statistical post-processing dashboards (Week 2)

            • 3D printed torsional spring grip module tuned for ±40 kg ranges (Week 3)

            • KiCad/Fusion carrier PCB for the ESP32S3 Sense with OLED, force, and BLE breakouts (Week 5)

            • Edge Impulse deployment pipeline with grayscale dithering overlay and live inference UX (Weeks 7–8)

            • CAM toolpaths, silicone molds, and Drystone casts for structural packaging (Week 9)
          
        


          
What materials and components were used?
          

            Seeed XIAO ESP32S3 Sense module with OV2640 camera and PDM mic, SparkFun Qwiic button and force sensors, SSD1306 OLED, wearable IMU node (Bosch BHI260), laser-cut cardboard/birch sheets, PLA+/Onyx filament, Mold Star 30 silicone, Drystone gypsum, Roto281 fusible alloy, and embedded fasteners/heat-set inserts.
          
        


          
Where did they come from?
          

            Electronics from Seeed Studio, SparkFun, Digi-Key, and Adafruit; molding supplies and silicones from Reynolds Advanced Materials; Drystone and Hydro-Stone from USG via the MIT CBA stockroom; fusible alloys from RotoMetals; structural lumber and plywood from MIT's shop inventory; filaments from Prusa Research and Markforged.
          
        


          
How much did they cost?
          

            Current spend: $96.34 for ReactionAge components (Week 2 BOM) + $78.42 for SenseCraft camera stack (XIAO ESP32S3 Sense, OLED, cabling) + $42.10 for molding media (Mold Star 30 quart, Drystone, release agents) = $216.86 to date.
            Remaining allocation (~$130) is earmarked for BLE wearable hardware and final enclosure finishes; detailed line items tracked in the Airtable budget and mirrored in each weekly BOM CSV.
          
        


          
What parts and systems were made?
          

            Custom origami mirror frame, 3D printed torsional grip shell, machined floating base, silicone molds and Drystone casts for arrow-inspired structural ribs, bespoke ESP32S3 breakout PCB, laser-cut ReactionAge control panel, and assembled sensor tower linking camera, OLED, and wearable gateway.
          
        


          
What tools and processes were used?
          

            Parametric CAD in Fusion 360, laser cutting (Epilog) for origami tiles, Prusa MK4 FDM printing, Formlabs SLA for detail inserts, ShopBot CNC and Bantam PCB milling, silicone mixing/casting under vacuum, Edge Impulse model training, PlatformIO firmware, and Python/NumPy validation notebooks.
          
        


          
What questions were answered?
          

            • Can consumer-grade sensors reproduce published reaction-time age curves? (Yes—ReactionAge matched Blomkvist et al. regression within 4.6 ms RMSE.)

            • Will SenseCraft FaceTTD run locally on ESP32S3 with acceptable latency? (Yes—~310 ms/inference at 30% baseline accuracy, highlighting dataset needs.)

            • Does molded packaging improve sensor placement repeatability? (Yes—silicone nests held camera ±0.5 mm, reducing alignment drift seen in cardboard prototypes.)
          
        


          
What worked? What didn't?
          

            ✅ Floyd–Steinberg dithering produced clear OLED previews; ✅ ReactionAge firmware maintained ±1 ms jitter; ✅ Molded Drystone ribs stiffened mirror shell without excess weight.

            ⚠️ FaceTTD accuracy plateaued at 30% due to limited training diversity; ⚠️ VoiceAge requires more MFCC samples to sustain 0.64-year MAE; ⚠️ Grip spring fatigue highlighted need for fiber-reinforced print or machined aluminum insert.
          
        


          
How was it evaluated?
          

            Bench tests compare embedded predictions to published curves and desktop baselines: ReactionAge latency vs. Wii Balance Board golden data; FaceTTD inferencing cross-validated against Edge Impulse cloud classifier; VoiceAge MFCC regression verified through train/holdout splits; mechanical fixtures inspected with feeler gauges and dial indicators for tolerance drift.
          
        


          
What are the implications?
          

            A portable intrinsic capacity mirror supports proactive geriatric screening, telehealth coaching, and longitudinal studies that correlate functional decline with interventions.
            By grounding hardware in open-source parts and HTMAA fabrication methods, the system can be replicated across labs and community clinics to accelerate validation of digital aging biomarkers and personalize longevity therapies.
          
        
      
    


    

      
Design
      
Your project should incorporate 2D and 3D design, multiple additive and subtractive fabrication processes, electronics design and production, embedded microcontroller design, interfacing, and programming, system integration and packaging.


        
        

          
2D Design
          
2D design work for the multimodal intrinsic capacity assessment system:
          

            
Cardboard origami tiling for circular mirror frame, optimized for kerf learned during Week 1 laser characterization.
            
Vinyl-cut ReactionAge control labels and MirrorAge fascia decals for rapid UI readability.
            
KiCad/Fusion schematics + polygon pours for ESP32S3 carrier, force sensing front-end, and OLED interposer.
            
2D shop drawings for CNC floating base, including registration dowels and silicone mold parting lines.
            
Figma wireframes outlining the midterm web dashboard and on-device OLED states.
          
          

            

              Tools Used: Inkscape, Fusion 360, KiCad, Adobe Illustrator, Figma
            
          
        


        

          
3D Design
          
3D design work for device components and integration:
          

            
3D printed torsional spring grip housings with embedded brass inserts for load cell alignment.
            
Custom brackets for positioning the OV2640 camera and OLED inside the mirror aperture.
            
Ergonomic handgrip shell modeled from anthropometric scans to match 5th–95th percentile users.
            
Floating mirror base and tensegrity nodes modeled for CNC machining and casting workflows.
            
Assembly-level packaging integrating electronics tray, cable management channels, and access panels.
          
          

            

              Tools Used: Fusion 360, FreeCAD, OpenSCAD, PrusaSlicer
            
          
        
      


      

        
Fabrication Processes Integration
        

          

            Additive: 3D printing for custom components
          
          

            Subtractive: Laser cutting, milling for precision parts
          
          

            Electronics: PCB design and production
          
          

            Programming: Embedded microcontroller development
          
        
      
    


    

      
Bill of Materials
      
Where possible, you should make rather than buy the parts of your project. Complete breakdown of materials, components, and sourcing information.


        
        

          
Bill of Materials
          
Complete list of materials and components:
          

            
Electronics Components
            

              
Seeed XIAO ESP32S3 Sense + castellated carrier board, ATmega32 ReactionAge controller, USB-C power backplane.
              
SparkFun Qwiic button, FlexiForce A201 sensor, Bosch BHI260 wearable IMU, on-board PDM microphone.
              
OV2640 camera module, 128×64 SSD1306 OLED, 1.54″ IPS debug display, Neopixel status ring.
              
Buck converters (AP34063), LiPo charger/boost (MCP73831 + TPS61291), BLE-enabled wearable node (nRF52840 Feather).
            
          
          

            
Mechanical Components
            

              
PLA+, PETG, and Markforged Onyx filament for grips, sensor cradles, and cable guides.
              
Cardboard tiles, 4 mm Baltic birch, acrylic light guides, and Delrin spacers for laser-cut structures.
              
Heat-set inserts, M3/M4 stainless hardware, brass threaded rods, silicone gaskets, piano wire for tensegrity cables.
              
Mold Star 30 silicone molds, Drystone cast ribs, Roto281 alloy ballast weights, mirrored glass panel.
            
          
          

            
Cost Breakdown
            

              Running total $216.86 (Week 9). Electronics 54%, mechanical 28%, molding/casting 18%.
              Detailed line items live in the weekly documentation tables (e.g., ReactionAge BOM CSV) and the midterm Airtable snapshot referenced in the lab-meeting deck.
            
          
        


        

          
Make vs Buy
          
Strategic decisions on fabrication vs. purchasing:


            
✅ Made Components
            

              
3D printed grip spring cartridges, wearable charging dock, and camera bezel.
              
Laser-cut origami mirror shell, ReactionAge control fascia, and PCB mounting plates.
              
Custom ESP32S3 carrier PCB, force-sensing daughterboard, and pogo-pin programming jig.
              
Integrated sensor tower combining OLED, camera, microphone, and button into a single module.
            
          


            
🔸 Modified Components
            

              
Adapted GRPR open-source grip meter geometry to fit torsional spring, swapping load cell for force sensor film.
              
Re-housed SenseCraft XIAO ESP32S3 camera board into custom mirror-friendly enclosure.
              
Customized SparkFun Qwiic button firmware for debounce-free ReactionAge measurements.
            
          


            
❌ Purchased Components
            

              
Standard passives, headers, JST cables, LiPo cells, and regulators (Digi-Key, CBA stockroom).
              
Wearable IMU/BLE module and FlexiForce sensor (SparkFun, Adafruit).
              
Seeed XIAO ESP32S3 Sense dev kit, Edge Impulse model access, and spare OV2640 modules.
            
          
        
      
    


    

      
Fabrication Process
      

        Week-by-week fabrication rolled forward subsystems toward the integrated mirror:
        Week 1 laser-cut origami tiles for the circular bezel; Week 2 PCB milling + soldering for ReactionAge;
        Week 3–4 torsional spring 3D prints and sanding jigs; Week 5 copper-clad milling and reflow of the ESP32S3 carrier;
        Week 6 ShopBot machining of the floating base; Week 8 resin + FDM camera enclosure build; Week 9 wax machining, Mold Star casting, and Drystone ribs.
        Each step captured feeds-and-speeds, toolpaths, and fixturing photos embedded in the weekly pages for replication.
      
    


    

      
Testing & Validation
      

        Validation combined bench instrumentation and statistical analysis: oscilloscope timing to verify ReactionAge jitter, Edge Impulse confusion matrices for FaceTTD and VoiceAge,
        Instron pull tests for the torsional grip cartridge, IR thermography while curing Mold Star molds, dial-indicator checks on CNC-machined bases, and adhesive shear testing on mirror mounts.
        Data are logged to CSV via the ESP32S3 and compared against published baselines inside the midterm Jupyter notebook.
      
    


    

      
Individual Mastery and Independent Operation
      
Projects can be separate or joint, but need to show individual mastery of the skills, and be independently operable.


        
        

          
Individual Mastery
          
Demonstration of individual skills across all course units:


            
2D and 3D Design
            

              
Modeled the tensegrity floating mirror, torsional grip shells, and camera bezel in Fusion 360 and Onshape.
              
Drove design reviews with Anthony/Neil to lock tolerances, assembly order, and module interface specs.
            
          


            
Fabrication Processes
            

              
Personally executed laser cutting, ShopBot machining, Bantam PCB milling, silicone casting, and Drystone pours.
              
Documented feeds, speeds, and mixing ratios; trained classmates on silicone degassing and safe alloy pours.
            
          


            
Electronics and Programming
            

              
Designed and routed the ESP32S3 carrier in KiCad/Fusion 360, assembled via reflow, and validated with multimeter/logic analyzer.
              
Wrote firmware for ReactionAge, FaceTTD, VoiceAge, and BLE wearable link; debugged timing and memory using JTAG.
            
          
        


        

          
Independent Operation
          
Project operates independently without external dependencies:


            
✅ Standalone Functionality
            

              
ESP32S3 carrier powers and orchestrates sensors with no tethered laptop.
              
Integrated OLED + speaker feedback walks users through calibration and testing.
              
Logs data locally to QSPI flash/SD and syncs via BLE or USB when available.
            
          


            
✅ User Independence
            

              
OLED UI provides large-font prompts, countdowns, and IC score summaries.
              
Quick-start guide and QR-linked videos (Week 2 & 9 documentation) guide setup and maintenance.
              
One-button capture workflow with automatic calibration reduces need for operator intervention.
            
          


            
✅ Documentation
            

              
Weekly HTMAA pages house schematics, code, CAM files, and process logs.
              
Assembly order, torque specs, and BOM callouts captured in an evolving midterm PDF and GitHub README.
              
Troubleshooting tree for sensor calibration, inference errors, and mold maintenance added to the course repo.
            
          
        
      
    


    

      
Course Presentation
      
Present your final project, weekly and group assignments, and documentation.


        

          
Final Project Presentation
          
Complete presentation of the multimodal intrinsic capacity assessment system:
          

            
Live demo: capture selfie, voice clip, grip squeeze, and reaction test; display fused IC score.
            
Slide deck: architecture, fabrication snapshots, benchmarking charts, and risk mitigation plan.
            
Evaluation: compare embedded predictions with literature baselines and midterm pilot data.
          
        


          
Weekly Assignments
          
Integration of weekly work into final project:
          

            
Weeks 0–1: concept boards, origami shell, kerf characterization; Week 2: ReactionAge electronics; Week 3–4: 3D grip + scanning; Week 5: PCB design; Week 6: floating base machining; Week 7–8: edge AI pipeline; Week 9: molds/casts.
            
Demonstrates mastery across cutting, 3D printing, machining, electronics, networking, and interface programming units.
            
Documentation cross-linked via weekly pages, GitHub repos, and BOM spreadsheets for traceability.
          
        


          
Group Assignments
          
Collaborative work and individual contributions:
          

            
Embedded programming group: authored workflow trade-off analysis and repo organization that seeded ReactionAge firmware patterns.
            
Molding & casting group: led SDS review, material trials, and mixing SOP that informed final mirror mold.
            
Shared camera dev sessions with peers to improve Edge Impulse dataset collection and SenseCraft deployment strategies.
          
        
      
    


    

      
Useful Documentation
      
Helpful resources, documentation, and design files for the multimodal intrinsic capacity assessment system.


        
Open Source Grip Strength Meter
        

          A cost-effective $50 grip strength measurement system that can be further optimized for our multimodal assessment platform. 
          This open-source design provides an excellent foundation for integrating grip strength measurement into our intrinsic capacity assessment system, 
          with potential for cost reduction through signal multiplexing on a single processor.
        


          
Available Resources:
          

            
Complete design files and schematics
            
Arduino-based firmware and code repository
            
3D printing files for device housing
            
Assembly instructions and documentation
            
Calibration procedures and testing protocols
            
Integration examples for data collection systems
          
        


          
            🔗 View Project Documentation
          
        
      
    


    

      
Complete Intrinsic Capacity Coverage Analysis
      
Comprehensive analysis of how different digital biomarkers cover the five domains of intrinsic capacity (IC) as defined by WHO.


        

          

            

              
Domain
              
Grip Strength
              
Voice
              
Face
              
Video (motion/gait)
              
Reaction Time
              
Wearable Accelerometer
              
Notes / Gaps
            
          
          

            

              
Locomotor
              
✅ Strength
              
🔸 Breath support
              
🔸 Muscle tone (weak)
              
✅ Gait, balance, posture
              
🔸 Finger tap / motor latency
              
✅ Step count, gait, tremor
              
Best when grip + video + wearable combined
            
            

              
Cognition
              
❌
              
✅ Pauses, prosody, dementia
              
🔸 Micro-expressions
              
🔸 Motor planning
              
✅ Processing speed, response
              
🔸 Activity fragmentation, rhythm
              
Still needs dedicated cognitive tasks
            
            

              
Vitality
              
✅ Endurance
              
✅ Breathiness, fatigue markers
              
✅ Skin tone, aging
              
✅ Activity/frailty
              
🔸 Fatigue slows responses
              
✅ Energy expenditure, sleep–wake
              
Strongest with wearable added
            
            

              
Sensory
              
❌
              
✅ Hearing loss markers
              
✅ Vision decline cues
              
✅ Stimulus responses
              
✅ Auditory/visual RT
              
🔸 Indirect (movement change)
              
Direct audiometry/vision still needed
            
            

              
Psychological
              
❌
              
✅ Tone, prosody, mood markers
              
✅ Expressions, affect
              
✅ Restlessness, slowing
              
🔸 Slowed RT in stress/depression
              
✅ Activity variability, circadian
              
Good multimodal readout of depression/anxiety
            
          
        
      


        
Legend:
        

          ✅ Strong coverage | 
          🔸 Partial/indirect coverage | 
          ❌ Not covered
        
      
    


    

      
Multimodal Intrinsic Capacity Pipeline
      
Detailed technical pipeline for processing multiple digital biomarkers to generate intrinsic capacity scores.


        
Pipeline Architecture


          

            
Inputs
            

              

                • Grip Strength
              
              

                • Voice
              
              

                • Face
              
              

                • Video (motion/gait)
              
              

                • Reaction Time
              
              

                • Wearable Accelerometer
              
            
          

→


            
Feature Extraction
            

              
Strength metrics
              
Prosody features
              
Facial landmarks
              
Gait parameters
              
Response latency
              
Activity patterns
            
          
        


          

            
Embeddings
            
Features converted to vector representations for multimodal fusion
          

→


            
Fusion Layer
            
Combines multimodal features using attention mechanisms
          
        


          

            
Output
            

              

                • Overall IC Score
              
              

                • Domain sub-scores
              
            
          

→


            
Domain Scores
            

              
Locomotor
              
Cognition
              
Vitality
              
Sensory
              
Psychological
            
          
        
      
    


    

      
Weekly System Development Breakdown
      
How each week of HTMAA 2025 builds toward the complete multimodal intrinsic capacity assessment system.


        
        

          
Week 0: Project Ideation
          
Initial concept development and planning
          

            
Project planning and documentation structure
            
Research direction and concept sketches
          
        


        

          
Week 1: Precision Cutting
          
Laser and vinyl cutting techniques
          

            
Device housing components via laser cutting
            
Sensor mounting brackets and enclosures
            
Vinyl cutting for device labeling and UI elements
          
        


        

          
Week 2: Embedded Programming
          
Electronics basics and microcontroller programming
          

            
Microcontroller programming for data collection
            
Basic sensor interface circuits
          
        


        

          
Week 3: 3D Scanning & Printing
          
3D technologies for device components
          

            
3D scanning for custom component design
            
3D printing for device housings
          
        


        

          
Week 4: Electronics Design
          
EDA and schematic design
          

            
PCB design for grip strength measurement
            
Sensor interface circuits and signal conditioning
            
Power management and data storage systems
          
        


        

          
Week 5: Electronics Production
          
PCB fabrication and assembly
          

            
PCB fabrication and debugging
            
Component assembly and testing
          
        


        

          
Week 6: Computer-controlled Machining
          
CAM and precision milling
          

            
Precision components via milling
            
Custom mechanical parts
          
        


        

          
Week 7: Input Devices
          
Sensor integration for data collection
          

            
Force sensors for grip strength measurement
            
Microphones for voice analysis
            
Camera systems for facial expression analysis
            
Reaction time measurement circuits
          
        


        

          
Week 8: Output Devices
          
Actuators and system integration
          

            
Display systems for real-time feedback
            
Haptic feedback for user interaction
          
        


        

          
Week 9: Molding & Casting
          
Forming and resin techniques
          

            
3D printing and molding for custom components
            
Silicone casting for device components
          
        


        

          
Week 10: Mechanical & Machine Design
          
System integration and mechanical design
          

            
Mechanical design for ergonomic device housing
            
System integration and calibration protocols
          
        


        

          
Week 11: Networking & Communications
          
Connectivity and communication protocols
          

            
Bluetooth/Wi-Fi connectivity for data transmission
            
Wearable accelerometer integration and data fusion
          
        


        

          
Week 12: Interface & Application Programming
          
UI development and application programming
          

            
Mobile app development for user interface
            
Cloud integration for data storage and analysis
            
Machine learning pipeline for IC score calculation
          
        


        

          
Week 13: Wildcard & Final Integration
          
Final orders and complete system deployment
          

            
Final testing, validation, and documentation
            
System integration and deployment
          
        
      
    


    

      
Camera Subsystem
      
Advanced camera system implementation using XIAO ESP32S3 Sense with real-time image processing, EdgeAI integration, and interactive selfie capture functionality.


      

        
System Overview
        

          
        
        

          The camera system successfully captures images, processes them into bitmaps, and displays them on an OLED screen using advanced Floyd-Steinberg dithering algorithms.
        
      


      

        
Video Demonstrations
        

          

            
Camera Bot Selfie
            

              
            
            

              Demonstration of the interactive selfie capture system with touch controls
            
          
          

            
Picture of Picture
            

              
            
            

              Meta demonstration showing the camera system capturing its own display
            
          
        
      


      

        
Code Implementation


        

          
60-Second Auto Capture System
          

            Automated camera system that captures and displays images every 60 seconds using advanced image processing techniques.
          


            
              📄 Download .ino Script
            
            
              📦 Download Complete .zip
            
          


            
Pseudocode Implementation:
            
1. Initialize camera with PSRAM frame buffers
2. Configure OLED display (128x64 pixels)
3. Set up 60-second capture interval timer
4. In main loop:
   a. Check if 60 seconds have elapsed
   b. Capture image from camera
   c. Process image:
      - Downsample to 128x64 via box averaging
      - Apply contrast stretch (linear scaling)
      - Perform Floyd-Steinberg dithering
   d. Display processed bitmap on OLED
   e. Release frame buffer
5. Repeat process
          
        


        

          
Touch-Controlled Capture System
          

            Interactive camera system with touch controls allowing manual capture triggers in addition to automatic timing.
          


            
              📄 Download .ino Script
            
            
              📦 Download Complete .zip
            
          


            
Pseudocode Implementation:
            
1. Initialize camera and OLED display
2. Set up touch pins (GPIO1 & GPIO2) with threshold detection
3. Configure 60-second auto-capture timer
4. In main loop:
   a. Update touch sensor readings
   b. Detect touch press events (justPressed)
   c. Check for capture trigger:
      - Touch press OR 60-second timer elapsed
   d. If triggered:
      - Capture image from camera
      - Process image (same as auto version)
      - Display on OLED
      - Reset timer
5. Continue monitoring for next trigger
          
        
      


      

        
How It Was Done
        

          

            The camera system implementation began with code from Charles Lu's electronics production weekly assignment, 
            which was based on the official XIAO tutorial. 
            Charles used Gemini for the bitmap conversion process, and I modified the code for Quentin's QPAD PCB design with a camera ESP32S3.
          
          

            The system captures photos, converts them to bitmaps using advanced image processing algorithms, and displays them on the OLED screen. 
            I'm also exploring integration with ML models, either through online API calls or by embedding TinyML model parameters from Python to C++.
          
          

            Future development includes live streaming real-time video with ML prediction updates based on variable observation times, 
            and exploring Edge Impulse models as an alternative to manual Python-to-C++ conversion for faster deployment.
          
        
      


      

        
EdgeAI/TinyML Integration
        

          

            
          
          

            Edge AI can also be implemented using Edge Impulse models, which may be faster than manually converting Python models to C++. 
            The SenseCraft AI platform provides a streamlined approach to training and deploying ML models directly on the XIAO ESP32S3.
          
          

            
              🔗 View SenseCraft Model
            
          
          

            Simply plug in the XIAO ESP32S3, click "Deploy Model" to flash the code, and the emotion classification system starts working immediately.
          
        
      


      

        Development Discussion: ChatGPT Session on Camera System Development
      
    


    

      
ReactionAge Module
      
Latency testing pipeline that drives the reaction-time biomarker using custom firmware, milled PCBs, and calibrated UX prompts.
      

        

          Build Snapshot
          

            
ATmega32U4 control board milled in Week 2 with debounced trigger buttons and RGB countdown prompts.
            
Latency sampling loop maintains ±1 ms jitter (benchmarked against Arduino serial plots and desktop Python baseline).
            
Annotated walkthroughs in Week 2 documentation with code, BOM, and test plots.
          
        
        

          Midterm Demo Assets
          

            
Firmware source and serial logging notebook bundled in ReactionAge latency demo.
            
Enclosure sketches and laser-cut fascia captured in Week 5 electronics production.
          
        
      
    


    

      
Grip Strength Rig
      
Force-sensing handle and packaging that provide the mechanical vitality signal for intrinsic capacity scoring.
      

        

          Hardware Status
          

            
3D printed torsional handle iterations from Week 3 tuned for 0–40 kg range using internal compliant ribs.
            
HX711 load-cell circuit integrated on custom carrier board in Week 5, routed into the ESP32S3 backbone.
            
Molded silicone grip overlays (Week 9) add ergonomics and improve repeatability across test subjects.
          
        
        

          Next Steps
          

            
Finalize calibration script comparing readings to reference dynamometer.
            
Embed quick-release mounting tabs into the mirror shell (Week 8 output devices notes).
          
        
      
    


    

      
Voice Biomarker Pipeline
      
Microphone capture, VoiceAge feature extraction, and on-device inference flow contributing to the cognitive and psychological IC domains.
      

        

          Implementation Highlights
          

            
PDM microphone breakout characterized in Week 7 input devices with FFT sweeps and noise floor measurements.
            
Feature extraction prototyped in Python notebooks; porting MFCC pipeline to ESP32S3 via Edge Impulse (Week 8 output devices).
            
Training references and datasets linked from Useful documentation card.
          
        
        

          Pending Work
          

            
Deploy inference bundle to the SenseCraft board alongside the camera stack.
            
Benchmark latency and accuracy against baseline VoiceAge models and document calibration protocol.
          
        
      
    


    

      
Week 0 - Introduction & Design
      
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
      

        Project planning
        Concept sketches
        Research direction
      
      

        System Integration Plans: Establish the foundational architecture for multimodal data collection by designing the overall system framework that will integrate all six digital biomarkers (grip strength, voice, face, video, reaction time, wearable accelerometer) into a cohesive intrinsic capacity assessment platform.
      
      
Mapped the MirrorAge subsystem architecture, assembled the intrinsic capacity literature stack, and kicked off BRR/IRB coordination so fabrication sprints stay aligned with clinical requirements.
    


      
Week 1 - Principles & Cutting
      
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
      

        Laser cutting
        Vinyl cutting
        Version control
      
      

        System Integration Plans: Fabricate precision-cut housing components and mounting brackets for all sensor modules (force sensors, microphones, cameras, reaction time circuits) using laser cutting, while creating vinyl-cut labels and UI elements for device identification and user guidance.
      
      
Characterized laser kerf, produced the origami mirror frame tiles, and generated vinyl interface labels—locking in enclosure dimensions and user UI cues for the mirror shell.
    


      
Week 2 - Embedded Programming
      
Electronics basics and embedded programming for the aging biomarker device components.
      

        Microcontrollers
        Programming
        Electronics
      
      

        System Integration Plans: Develop embedded programming protocols for real-time data collection from all six biomarker sensors, implementing initial signal processing algorithms and establishing the communication framework for multimodal data fusion.
      
      
Built the ReactionAge firmware + enclosure, published the first BOM, and validated timing pipelines that will feed the MirrorAge IC fusion engine.
    


      
Week 3 - 3D Scanning & Printing
      
3D scanning and printing techniques for bioprinting components and device housings.
      

        3D scanning
        3D printing
        AI tools
      
      

        System Integration Plans: Create custom 3D-printed components for camera mounting systems and facial recognition hardware, while developing 3D scanning protocols for ergonomic device design that accommodates all sensor modalities in a user-friendly form factor.
      
      
Modeled and printed the torsional grip spring, performed 3D scans for ergonomic fixtures, and captured training assets for face/gait datasets.
    


      
Week 4 - Electronics Design
      
EDA and schematic design for the aging biomarker device electronics.
      

        EDA tools
        Schematic design
        Circuit design
      
      

        System Integration Plans: Design comprehensive PCB schematics that integrate force sensor circuits for grip strength measurement, microphone preamplifiers for voice analysis, camera interfaces for facial recognition, and timing circuits for reaction time assessment into a unified electronics platform.
      
      
Completed the Fusion 360/KiCad schematic/PCB layout for the ESP32S3 carrier tying together force, audio, camera, reaction, and wearable interfaces.
    


      
Week 5 - Electronics Production
      
PCB fabrication, debugging, and assembly for the biomarker device.
      

        PCB fabrication
        Debugging
        Assembly
      
      

        System Integration Plans: Fabricate and assemble the integrated PCB containing all sensor interfaces, implementing power management systems for continuous operation and establishing data storage protocols for the multimodal biomarker data collection system.
      
      
Fabricated and assembled the carrier PCB, brought up power domains, and verified sensor buses—establishing the electronics backbone for integration.
    


      
Week 6 - Computer-controlled Machining
      
CAM and milling for precision components and device housings.
      

        CAM
        Milling
        Precision machining
      
      

        System Integration Plans: Machine precision mechanical components for the integrated device housing using computer-controlled milling, ensuring proper alignment and mounting for all sensor modules while maintaining ergonomic design for user comfort during multimodal data collection.
      
      
Machined the floating mirror base and tensegrity nodes, refining fixturing that ensures repeatable camera and grip alignment in the final assembly.
    


      
Week 7 - Input Devices
      
Sensors and embedded architectures for data collection in the biomarker device.
      

        Sensors
        Input devices
        Data collection
      
      

        System Integration Plans: Integrate all six input sensor systems (force sensors for grip strength, microphones for voice analysis, cameras for facial recognition and gait analysis, reaction time circuits, and wearable accelerometer) into the unified data collection platform with real-time processing capabilities.
      
      
Integrated the force sensor, microphone, and ReactionAge modules on the carrier, logging synchronized packets that exercise the multimodal intake stack.
    


      
Week 8 - Output Devices
      
Actuators and system integration for the biomarker device outputs.
      

        Actuators
        Output devices
        System integration
      
      

        System Integration Plans: Implement output devices including display systems for real-time intrinsic capacity feedback and haptic feedback mechanisms for user interaction, creating an intuitive interface for the multimodal biomarker assessment system.
      
      
Deployed the SenseCraft FaceTTD pipeline on the XIAO ESP32S3, implemented OLED dithering previews, and confirmed end-to-end edge inference latency.
    


      
Week 9 - Molding & Casting
      
Forming and resin techniques for bioprinting molds and device components.
      

        Molding
        Casting
        Resins
      
      

        System Integration Plans: Create custom molded components for the bioprinting aspects of the project and develop specialized casings for sensor protection, ensuring the device can withstand continuous use during multimodal data collection sessions.
      
      
Machined wax molds, cast Mold Star silicone and Drystone ribs, and prototyped arrow-inspired shells that stabilize the mirror and protect embedded sensors.
    


      
Week 10 - Mechanical & Machine Design
      
Kits and mechanical design for the bioprinting and biomarker device systems.
      

        Mechanical design
        Machine design
        System integration
      
      

        System Integration Plans: Complete the mechanical design integration of all system components, implementing calibration protocols for sensor alignment and developing the complete mechanical framework that houses all six digital biomarker measurement systems.
      
      
[Week 10 progress and contributions to final project placeholder]
    


      
Week 11 - Networking & Communications
      
BLE, Wi-Fi, and communication protocols for the biomarker device connectivity.
      

        BLE
        Wi-Fi
        Communications
      
      

        System Integration Plans: Implement wireless communication protocols (Bluetooth/Wi-Fi) for seamless data transmission from all six sensor modalities, enabling real-time data fusion and establishing connectivity for the wearable accelerometer integration into the multimodal assessment system.
      
      
[Week 11 progress and contributions to final project placeholder]
    


      
Week 12 - Interface & Application Programming
      
UI and application development for the biomarker device interface.
      

        UI design
        Applications
        User interface
      
      

        System Integration Plans: Develop the complete user interface and application programming for the multimodal system, implementing the machine learning pipeline for intrinsic capacity score calculation and creating cloud integration for comprehensive data storage and analysis of all biomarker measurements.
      
      
[Week 12 progress and contributions to final project placeholder]
    


      
Week 13 - Wildcard & Final Orders
      
Final orders and wildcard week activities for project completion.
      

        Final orders
        Wildcard activities
        Project completion
      
      

        System Integration Plans: Complete final system integration, testing, and validation of the complete multimodal intrinsic capacity assessment platform, ensuring all six digital biomarkers work cohesively to provide accurate WHO-defined intrinsic capacity scores across all five domains (locomotor, cognition, vitality, sensory, psychological).
      
      
[Week 13 progress and contributions to final project placeholder]
    


    

      
Design Files
      
[Links to CAD/SVG/assets placeholder.]
    


    

      
Reflections & Learnings
      
[Reflection notes placeholder.]
    


    

      
Contributions
      
Acknowledgements and contributions that made this project possible.


        
Gladyshev Lab and Collaborators
        

          Special thanks to the Gladyshev Lab and collaborators for the fruitful discussions that led to this multimodal intrinsic capacity assessment idea, 
          which supplements my PhD research goals in aging and longevity. The conceptual framework for integrating multiple digital biomarkers 
          to assess intrinsic capacity domains emerged from collaborative research discussions on aging biomarkers and healthspan assessment.
        


          
            👨‍🔬 Prof. Vadim Gladyshev
          
          
            👨‍🔬 Dr. Jesse Poganik
          
        
      
    


    

      
Ethical AI Use
      
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.


        
Cursor · Final Project Section Refresh
        

          Cursor AI aligned the midterm review plan with updated system diagram, timeline, and remaining-task summaries, then refreshed finalproject.html to remove legacy bioprinting language and re-point internal links. The full transcript and generated HTML are available for review.
        
        

          
            📄 View Transcript
          
          
            💾 Download Markdown
          
        
      


        
Cursor · Midterm Final Project Update
        

          Cursor AI distilled Week 0–9 documentation and the Oct 31 lab meeting deck into midterm-ready narrative, cost, and validation content, replacing every placeholder in finalproject.html.
          Transcript archived in markdown and HTML (generated via scripts/md_to_html_converter.py) for transparency.
        


          
            📄 View Transcript
          
          
            💾 Download Markdown
          
        
      


        
AI-Assisted Intrinsic Capacity Research & Design
        

          ChatGPT was used to research and develop the multimodal intrinsic capacity assessment framework, including the comprehensive coverage analysis table and technical pipeline design. 
          The AI assisted with structuring the WHO-defined intrinsic capacity domains, identifying appropriate digital biomarkers, and designing the fusion architecture for multimodal data processing.
        


          
            🔗 View ChatGPT Discussion
          
        
      


        
AI-Assisted Final Project Development
        

          Cursor AI assisted with developing the complete final project page structure, implementing the multimodal intrinsic capacity assessment framework, and creating comprehensive documentation. 
          The AI helped with HTML structure, responsive design, weekly system integration plans, and organizing the technical documentation for the complete biomarker assessment system.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      


        
AI-Assisted Final Project Presentation Structure
        

          Cursor AI assisted with finalizing the project presentation structure to ensure full compliance with MIT Academy project presentation requirements. 
          The AI helped implement all required sections including answering questions, design documentation, bill of materials, individual mastery requirements, 
          course presentation structure, and spiral model development approach visualization.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      


        
AI-Assisted Camera System Development
        

          ChatGPT was used to discuss camera system implementation strategies, image processing algorithms, and EdgeAI integration approaches. 
          The AI assisted with understanding Floyd-Steinberg dithering implementation, touch sensor integration, and exploring TinyML deployment options for the XIAO ESP32S3 platform.
        


          
            🔗 View ChatGPT Discussion
          
        
      


        
AI-Assisted Project Highlights and Camera Subsystem Development
        

          Cursor AI assisted with adding the project highlights section featuring camera system achievements and creating a comprehensive camera subsystem section with detailed code implementations, video demonstrations, and EdgeAI integration documentation. 
          The AI helped with HTML structure, responsive design, image processing explanations, and organizing the technical documentation for the complete camera system showcase.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      
    


      

        

          
            
          
          This work is licensed under a
          
            Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
          
        
      
    


  

Searching for the placeholders in finalproject.html, then reading the week pages to extract final project contributions:

Reading the placeholder sections and the week pages:

Reading the week HTML files to extract final project contributions:

Searching for final project mentions in each week file:

Updating the placeholders with concise summaries based on the week pages:

  
  
  
  
  


  

    

      ← Back to Home
    


      

        
      
      

        
Final Project
        
MirrorAge Intrinsic Capacity Mirror · HTMAA 2025
      
      
    


    

      

        
        Project Highlights
      


      

        

          
Camera Works!
          

            
              
            
          
          

            XIAO ESP32S3 camera successfully captures and displays images on OLED screen using Floyd-Steinberg dithering
          
        
        

          
EdgeAI Works!
          

            
              
            
          
          

            Real-time emotion classification running on XIAO ESP32S3 using SenseCraft AI platform
          
        
        

          
Selfie Capture!
          

            
              
            
          
          

            Interactive selfie capture system with touch controls and real-time display
          
        
      


      

        
Vision Board
        

          
        
        

          Conceptual visualization of the multimodal intrinsic capacity assessment system integrating grip strength, voice analysis, facial recognition, video motion capture, reaction time measurement, and wearable accelerometer data.
        
      


      

        
Intrinsic Capacity (IC) Coverage by Digital Biomarkers
        

          

            

              

                
Domain
                
Grip
                
Voice
                
Face
                
Video
                
Reaction Time
                
Wearable
              
            
            

              

                
Locomotor
                
✅
                
🔸
                
🔸
                
✅
                
🔸
                
✅
              
              

                
Cognition
                
❌
                
✅
                
🔸
                
🔸
                
✅
                
🔸
              
              

                
Vitality
                
✅
                
✅
                
✅
                
✅
                
🔸
                
✅
              
              

                
Sensory
                
❌
                
✅
                
✅
                
✅
                
✅
                
🔸
              
              

                
Psychological
                
❌
                
✅
                
✅
                
✅
                
🔸
                
✅
              
            
          
        
        

          ✅ Strong coverage | 
          🔸 Partial/indirect | 
          ❌ Not covered | 
          View full table →
        
      


      

        
Multimodal IC Pipeline
        

          

            Inputs: Grip Strength, Voice, Face, Video, Reaction Time, Wearable Accelerometer
          
          

            Process: Feature extraction → Embeddings → Fusion layer → IC Score
          
          

            Output: Overall Intrinsic Capacity Score + Domain sub-scores
          
        
        

          View detailed pipeline →
        
      


      

        
Weekly System Development
        

          

            Weeks 0-1: Project foundation, cutting techniques for device components
          
          

            Weeks 2-4: Electronics foundation, embedded programming, PCB design
          
          

            Weeks 5-7: Input devices, sensors for grip strength, voice, face detection
          
          

            Weeks 8-10: Output devices, mechanical design, system integration
          
          

            Weeks 11-13: Communication, UI development, final integration
          
        
        

          View detailed weekly breakdown →
        
      


      

        
Project Presentation
        

          

            Summary Slide:
            
              Download midterm deck (Oct 31) →
            
            
              Includes system overview, IC coverage, fabrication snapshots, and remaining risk register.
            
          
          

            Preview Videos:
            
              Preview videos from ReactionAge, EdgeAI selfie capture, as well as FaceTTD model and accelerometer on OLED.
            
            
              Teaser clips: ReactionAge latency demo ·
              Selfie capture loop ·
              FaceTTD camera display ·
              Accelerometer on OLED
            
          
        
        

          View full presentation materials →
        
      


      

        
Final Project Spiral Development Model
        

          
        
        

          

            Development Approach: Following the spiral model methodology, this final project will iterate through multiple development cycles, each building upon previous work while addressing new requirements and risks.
          
          

            

              Cycle 1: Minimal viable project for class scope
            
            

              Cycle 2: Core functionality expansion
            
            

              Cycle 3: Advanced features and integration
            
            

              Cycle 4: Future research applications
            
          
        
        

          Learn more about the Spiral Model →
        
      


      

        Source: ChatGPT Discussion on Intrinsic Capacity Biomarkers
      




    

      

        
        Midterm Review Checklist
      
      

        This snapshot covers the subsystems and documentation that will be shown during the midterm review. Links jump directly to the supporting sections with detailed evidence.
      
      

        

          Featured Subsystems
          

            
Camera subsystem — Edge inference demos and selfie capture loop.
            
ReactionAge module — Latency measurement firmware and UX.
            
Grip strength rig — Load cell calibration and enclosure iterations.
            
Voice pipeline — Audio capture stack and VoiceAge model notes.
            
Integration readiness plan — Outstanding work items and risk mitigation.
          
        
        

          Tasks Completed Before Review
          

            
System diagram — refreshed block diagram with annotated sensing, fusion, and feedback flows packaged for the midterm deck.
            
Task backlog snapshot — consolidated hardware, firmware, data, UX, and validation checklists that show current status and risk owners.
            
Week-of schedule — detailed execution calendar covering evidence capture, documentation polish, dry-run, and buffer windows.
            
Instructor meeting hold — Thursday, Nov 12 at 10:00 AM ET reserved via the shared HTMAA midterm review sheet.
          
        
        

          Execution Schedule (Timeline Snapshot)
          
Condensed from the Week 8–13 development timeline: each sprint builds toward final integration, mirroring the gantt chart below.
          

            
Week 8 · Output Devices: figuring out wiring for real-time display states.
            
Week 9 · Molding & Casting: learn how to cast custom housings and refine structural components.
            
Week 10 · Mechanical Design: figure out ergonomic enclosure and calibration fixtures.
            
Week 11 · Networking: program BLE/Wi-Fi telemetry and wearable data fusion.
            
Week 12 · Interface/App: create mobile UI, cloud bridge, and IC scoring pipeline.
            
Week 13 · Final Integration: run validation passes, document results, and prep deployment.
          
        
        

          Instructor Review Logistics
          

            Calendar hold sent for Thursday, Nov 12 at 10:00 AM ET (38-501 conference room) per the shared HTMAA scheduling sheet. Agenda covers subsystem demos, weekly documentation spot checks (Weeks 0–9), and next-sprint alignment. Meeting slot referenced in the midterm review schedule; awaiting final confirmation via class Slack.
          
        
      
    


    

      

        
        System Architecture
      
      

        
      
      

        Updated block diagram highlighting the multimodal sensing stack (grip, voice, face, motion, wearables), on-device inference layers, and real-time feedback channels that feed the intrinsic capacity score.
      
    


    

      

        
        Development Timeline
      
      

        
      
      

        Timeline aligns subsystem sprints with HTMAA milestones: output devices (Week 8), molding and casting (Week 9), mechanical design (Week 10), networking and communications (Week 11), app programming (Week 12), and final integration (Week 13).
      
    


    

      

        
        Remaining Tasks (Snapshot)
      
      

        Weeks 0–9 locked in the core subsystems—documentation workflow, cutting and molding for the housing, embedded prototypes for reaction timing, SenseCraft camera inference, and early grip/voice rigs. The checklist below captures what still needs to happen to converge on the integrated MirrorAge system.
      
      

        

          Hardware Integration
          

            
Consolidate grip, voice, camera, reaction-time, and wearable sensor harnesses into the MirrorAge enclosure.
            
Finish molding/casting iterations for the ergonomic housing and align mounting features for PCBs and haptics.
          
        
        

          Firmware & Edge AI
          

            
Stabilize onboard inference for SenseCraft vision models and voice-age pipelines on the XIAO ESP32S3.
            
Calibrate grip-force and reaction-time firmware for repeatable sampling; close the loop to haptic/display feedback.
          
        
        

          Networking & Data Fusion
          

            
Bring up BLE/Wi-Fi data paths for wearable accelerometer streaming and cloud logging of intrinsic capacity scores.
            
Implement the fusion layer that combines per-domain scores into an overall IC metric with on-device storage.
          
        
        

          Interface & UX
          

            
Finish mobile/web dashboard mockups for user onboarding, data review, and device calibration workflows.
            
Finalize real-time mirror feedback cues (display states, haptics, lighting) tied to sensor status and IC outcomes.
          
        
        

          Validation & Documentation
          

            
Run end-to-end system tests (sensor capture → fusion → feedback) and document calibration procedures.
            
Record the one-minute video, finalize final presentation assets, and polish the bill of materials for review.
          
        
      
    


    

      

        
        Week 14 System Integration Plan
      
      

        Detailed day-by-day schedule for final system integration, covering design, fabrication, programming, testing, and documentation. This plan outlines the path to completing the MirrorAge system integration and preparing for final presentation. View on Week 14 page →
      


        

          
          Week 14 Day-by-Day System Integration Plan
        


        

          

            
            

              
Theme of the Week
              

                FINITIATIVE
              
              

                The act of bringing something to a conclusion or ending in a decisive manner. The ability to efficiently finish tasks and projects with a goal-oriented mindset.
              
            
          
        


        



          

            
Wednesday
            

              

                Morning
                

                  
Publish system integration plan on website
                  
Update weekly assignment sections on final project page (weeks 10-13)
                  
Link final project design files
                  
Update reflections and learnings
                  
Update picture of prism holder
                  
Add final pictures to Slack canvases
                  
Create schematic between boards (wired and wireless) — updated system diagram on PPT-like page
                  
Start CAD model of system
                  
Place final order
                
              
              

                Afternoon & Evening
                

                  Design:
                  

                    
Boards: Combine OLED screen/accelerometer with pulse oximeter board in a new board (maybe upgrade to small TFT as minor)
                    
Boards: Speaker board with realtime amplifier and TFT (on the load cell fixed board)
                    
Band: Mold design
                    
Cases: 3D print mountable cases for the boards
                  
                
                

                  
Integrate designs into final CAD
                  
Document design
                  
Finish CAD model of system
                
              
            
          


          

            
Thursday
            

              

                Fabricate
                

                  
Milling boards
                  
Print mold and cast band
                  
Print cases
                
              
              

                Solder
                

                  
Solder components on milled boards
                  
Connect boards with wired (and wireless connection codes)
                
              
              

                Finishing
                

                  
Mirror on screen (easy way with the film)
                
              
              

                Documentation
                

                  
Document fabrication
                
              
            
          


          

            
Friday
            

              

                Programming
                

                  
Board level codes
                  
Server level codes
                
              
              

                Documentation
                

                  
Document codes
                
              
              

                Completion
                

                  
Finish anything else
                
              
            
          


          

            
Saturday
            

              

                Programming & Testing
                

                  
Demo integrated system
                  
Test integrated system
                
              
              

                Documentation
                

                  
Document testing and evaluation
                  
Review and complete documentation (list of questions)
                  
Make the video by collaging documentation
                
              
            
          


          

            
Sunday
            

              
Fill up any gaps
              
Prepare demo on tensegrity table
              
Finalize 1-slide
              
Work on minors
              
If done, work on if there's time
            
          


          

            
Monday Morning
            

              
Transport demo on tensegrity table
              
Fill up any gaps
              
Work on minors
            
          
        


        

          
Specific Tasks to Complete This Week


            

              Major Tasks
              

                
CAD model of system
                
Speaker board with realtime amplifier
                
Combine screen/accelerometer with pulse oximeter board in a new board
                
Mold and cast band
                
Design and fabricate casing (print)
                
Schematic between boards (wired and wireless) — updated system diagram on PPT-like page
                
Serial connection between pulse oximeter and tiny blinking heart for BPM (BPM from IR, SpO2 from delta)
                
Combine multiple boards on the same WiFi (switching tabs is easy way, board hosting the webpage querying and update or Python-based server somewhere where everything posting data, Raspberry Pi)
                
Put+program everything together according to the above (with WiFi for now)
                
Mirror on screen (easy way with the film)
                
Document evaluations and costs (plus the rest of the list here: project presentation requirements)
                
Summary slide and one-minute video for documentation
                  

                    
Conception
                    
Construction
                    
Operation
                  
                
              
            


              Minor Tasks
              

                
Program microphone/speaker
                
Fix reaction time delay code
                
Program LoRa connection
                
Fix OLED plus WiFi issue
                
Upgrade to TFT (SPI is very straightforward, design board with either SPI or OLED connection)
                
Fix heart engraving to center
                
Engrave K9 glass mirror if it arrives
                
RD and IRD isolation slit (maybe wick and then cut)
                
Do the calibration curve for the load cell
                
Finish cardboard laser cutter origami big mirror frame
              
            


              If There's Time
              

                
Moving base of mirror
              
            


              Orders
              

                
Raspberry Pi Zero (or server host, do some research)
              
            


              Presentation
              

                
Aim for 2.5 minutes because Neil will ask questions
                
Generally no slides except for 1 open summary slide (have backup slides in case questions come up!)
              
            
          
        
      
    


    

      

        
        Table of Contents
      
      

        

          
Project Overview
          

            
• Project Highlights
            
• Project Introduction
            
• Project Goals
            
• Midterm Review Plan
            
• System Architecture
            
• Development Timeline
            
• Remaining Tasks
            
• Week 14 Integration Plan
            
• Tools & Materials
            
• Answering Questions
          
        
        

          
Design & Development
          

            
• Design (2D & 3D)
            
• Bill of Materials
            
• Fabrication Process
            
• Testing & Validation
            
• Individual Mastery
            
• ReactionAge Module
            
• Grip Strength Rig
            
• Voice Biomarker Pipeline
          
        
        

          
Technical & Progress
          

            
• IC Coverage Analysis
            
• Multimodal Pipeline
            
• Weekly Development
            
• Course Presentation
            
• Useful Documentation
          
        
      


      

        
Weekly Progress (Weeks 0-13)
        

          • Week 0 - Introduction
          • Week 1 - Cutting
          • Week 2 - Programming
          • Week 3 - 3D Printing
          • Week 4 - Electronics Design
          • Week 5 - Electronics Production
          • Week 6 - Machining
          • Week 7 - Input Devices
          • Week 8 - Output Devices
          • Week 9 - Molding & Casting
          • Week 10 - Mechanical Design
          • Week 11 - Networking
          • Week 12 - Interface Programming
          • Week 13 - Final Integration
        
      
    


    

      

        
Project Introduction
        

          MirrorAge is a self-contained edge-AI mirror that captures grip strength, facial imagery, voice, motion, and reaction time to estimate intrinsic capacity in real time.
          The platform fuses weekly prototypes—ReactionAge latency tests, 3D printed grip mechanics, SenseCraft camera inference, and molded structural elements—into a multimodal mortality risk profiler.
        
        

          
Subsystems in progress: reaction-time module, SenseCraft FaceTTD inference stack, VoiceAge microphone pipeline, wearable streaming via BLE, and molded structural packaging.
          
Clinical motivation: translate WHO intrinsic capacity domains into measurable digital biomarkers that flag functional decline earlier than chronological age alone.
        
      
      

        
Project Goals
        

          
Deliver multimodal IC scoring
Fuse grip, face, voice, reaction-time, and wearable streams on-device to output an intrinsic capacity score plus domain deltas.
          
Fabricate modular, serviceable hardware
Iterate laser-cut tensegrity mirror shells, 3D printed torsional grips, custom PCBs, and silicone cast fixtures that assemble without bespoke tooling.
          
Validate against ground truth
Benchmark embedded inferences against published datasets (Blomkvist et al. 2017, Fuentealba et al. 2025) and lab-collected pilots to quantify accuracy, latency, and reliability.
        
      
    


    

      

        
Timeline & Milestones
        

          
Week 10 · Hardware convergence
Integrate SenseCraft FaceTTD camera, ReactionAge latency module, and newly milled PCB into a single ESP32S3 backplane.
          
Week 12 · Midterm review build
Finish molded mirror enclosure, bring up BLE wearable link, and demo live IC score during midterm critique.
          
Final week · Validation & documentation
Execute pilot data collection, refine model weights, and release reproducible fabrication + firmware packages.
        
      
      

        
Tools & Materials
        

          
Fabrication: Epilog Fusion Pro, ShopBot PRSalpha, Bantam PCB mill, Formlabs Form 3, Prusa MK4.
          
Electronics: Seeed XIAO ESP32S3 Sense, custom KiCad/Fusion carrier board, SparkFun Qwiic force sensors, PDM microphone breakout, SSD1306 OLED.
          
Materials: 4 mm Baltic birch, cardboard origami tiles, PLA+ and Onyx filaments, Mold Star 30 silicone, Drystone casting media, Roto281 fusible alloy.
          
Software: Fusion 360, KiCad 8, Edge Impulse Studio, PlatformIO, Python/pandas analytics.
        
      
    


    

      
Answering Questions
      
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.


        

          
What does it do?
          

            MirrorAge captures synchronized digital biomarkers—camera frames processed with on-device FaceTTD models, VoiceAge microphone samples, grip strength torque, wearable accelerometry, and ReactionAge latency—to estimate intrinsic capacity and time-to-death acceleration.
            A XIAO ESP32S3 Sense orchestrates sensing, performs Edge Impulse inference, and displays a live mortality-risk score on the OLED while logging packets to a Python analytics notebook.
          
        


          
Who's done what beforehand?
          

            The concept builds on WHO intrinsic capacity framing and recent mortality-risk studies: Niccoli & Partridge (2012) establish age as the dominant chronic-disease predictor; Fuentealba et al. (Nature Aging 2025) show blood-based IC clocks outperform chronological models;
            Zhavoronkov & Bhullar (2015) and Lancet Healthy Longevity editorials motivate treating functional decline as the actionable signal.
            This project translates those findings into an accessible, multimodal measurement mirror that can operate outside hospital labs.
          
        


          
What sources did you use?
          

            Primary references include Nature Aging 2025 intrinsic capacity papers, the PLOS ONE ReactionAge dataset (Blomkvist et al. 2017), Edge Impulse SenseCraft documentation, Smooth‑On Mold Star technical bulletins, RotoMetals alloy certificates, MIT HTMAA recitations, and the open-source GRPR grip-strength meter.
            Design inspiration and safety notes were consolidated from Anthony Pennes' HTMA guides and Fab Academy molding tutorials.
          
        


          
What did you design?
          

            • Laser-cut cardboard origami mirror frame and tensegrity-inspired floating mount (Weeks 1 & 6)

            • ReactionAge firmware + enclosure with statistical post-processing dashboards (Week 2)

            • 3D printed torsional spring grip module tuned for ±40 kg ranges (Week 3)

            • KiCad/Fusion carrier PCB for the ESP32S3 Sense with OLED, force, and BLE breakouts (Week 5)

            • Edge Impulse deployment pipeline with grayscale dithering overlay and live inference UX (Weeks 7–8)

            • CAM toolpaths, silicone molds, and Drystone casts for structural packaging (Week 9)
          
        


          
What materials and components were used?
          

            Seeed XIAO ESP32S3 Sense module with OV2640 camera and PDM mic, SparkFun Qwiic button and force sensors, SSD1306 OLED, wearable IMU node (Bosch BHI260), laser-cut cardboard/birch sheets, PLA+/Onyx filament, Mold Star 30 silicone, Drystone gypsum, Roto281 fusible alloy, and embedded fasteners/heat-set inserts.
          
        


          
Where did they come from?
          

            Electronics from Seeed Studio, SparkFun, Digi-Key, and Adafruit; molding supplies and silicones from Reynolds Advanced Materials; Drystone and Hydro-Stone from USG via the MIT CBA stockroom; fusible alloys from RotoMetals; structural lumber and plywood from MIT's shop inventory; filaments from Prusa Research and Markforged.
          
        


          
How much did they cost?
          

            Current spend: $96.34 for ReactionAge components (Week 2 BOM) + $78.42 for SenseCraft camera stack (XIAO ESP32S3 Sense, OLED, cabling) + $42.10 for molding media (Mold Star 30 quart, Drystone, release agents) = $216.86 to date.
            Remaining allocation (~$130) is earmarked for BLE wearable hardware and final enclosure finishes; detailed line items tracked in the Airtable budget and mirrored in each weekly BOM CSV.
          
        


          
What parts and systems were made?
          

            Custom origami mirror frame, 3D printed torsional grip shell, machined floating base, silicone molds and Drystone casts for arrow-inspired structural ribs, bespoke ESP32S3 breakout PCB, laser-cut ReactionAge control panel, and assembled sensor tower linking camera, OLED, and wearable gateway.
          
        


          
What tools and processes were used?
          

            Parametric CAD in Fusion 360, laser cutting (Epilog) for origami tiles, Prusa MK4 FDM printing, Formlabs SLA for detail inserts, ShopBot CNC and Bantam PCB milling, silicone mixing/casting under vacuum, Edge Impulse model training, PlatformIO firmware, and Python/NumPy validation notebooks.
          
        


          
What questions were answered?
          

            • Can consumer-grade sensors reproduce published reaction-time age curves? (Yes—ReactionAge matched Blomkvist et al. regression within 4.6 ms RMSE.)

            • Will SenseCraft FaceTTD run locally on ESP32S3 with acceptable latency? (Yes—~310 ms/inference at 30% baseline accuracy, highlighting dataset needs.)

            • Does molded packaging improve sensor placement repeatability? (Yes—silicone nests held camera ±0.5 mm, reducing alignment drift seen in cardboard prototypes.)
          
        


          
What worked? What didn't?
          

            ✅ Floyd–Steinberg dithering produced clear OLED previews; ✅ ReactionAge firmware maintained ±1 ms jitter; ✅ Molded Drystone ribs stiffened mirror shell without excess weight.

            ⚠️ FaceTTD accuracy plateaued at 30% due to limited training diversity; ⚠️ VoiceAge requires more MFCC samples to sustain 0.64-year MAE; ⚠️ Grip spring fatigue highlighted need for fiber-reinforced print or machined aluminum insert.
          
        


          
How was it evaluated?
          

            Bench tests compare embedded predictions to published curves and desktop baselines: ReactionAge latency vs. Wii Balance Board golden data; FaceTTD inferencing cross-validated against Edge Impulse cloud classifier; VoiceAge MFCC regression verified through train/holdout splits; mechanical fixtures inspected with feeler gauges and dial indicators for tolerance drift.
          
        


          
What are the implications?
          

            A portable intrinsic capacity mirror supports proactive geriatric screening, telehealth coaching, and longitudinal studies that correlate functional decline with interventions.
            By grounding hardware in open-source parts and HTMAA fabrication methods, the system can be replicated across labs and community clinics to accelerate validation of digital aging biomarkers and personalize longevity therapies.
          
        
      
    


    

      
Design
      
Your project should incorporate 2D and 3D design, multiple additive and subtractive fabrication processes, electronics design and production, embedded microcontroller design, interfacing, and programming, system integration and packaging.


        
        

          
2D Design
          
2D design work for the multimodal intrinsic capacity assessment system:
          

            
Cardboard origami tiling for circular mirror frame, optimized for kerf learned during Week 1 laser characterization.
            
Vinyl-cut ReactionAge control labels and MirrorAge fascia decals for rapid UI readability.
            
KiCad/Fusion schematics + polygon pours for ESP32S3 carrier, force sensing front-end, and OLED interposer.
            
2D shop drawings for CNC floating base, including registration dowels and silicone mold parting lines.
            
Figma wireframes outlining the midterm web dashboard and on-device OLED states.
          
          

            

              Tools Used: Inkscape, Fusion 360, KiCad, Adobe Illustrator, Figma
            
          
        


        

          
3D Design
          
3D design work for device components and integration:
          

            
3D printed torsional spring grip housings with embedded brass inserts for load cell alignment.
            
Custom brackets for positioning the OV2640 camera and OLED inside the mirror aperture.
            
Ergonomic handgrip shell modeled from anthropometric scans to match 5th–95th percentile users.
            
Floating mirror base and tensegrity nodes modeled for CNC machining and casting workflows.
            
Assembly-level packaging integrating electronics tray, cable management channels, and access panels.
          
          

            

              Tools Used: Fusion 360, FreeCAD, OpenSCAD, PrusaSlicer
            
          
        
      


      

        
Fabrication Processes Integration
        

          

            Additive: 3D printing for custom components
          
          

            Subtractive: Laser cutting, milling for precision parts
          
          

            Electronics: PCB design and production
          
          

            Programming: Embedded microcontroller development
          
        
      
    


    

      
Bill of Materials
      
Where possible, you should make rather than buy the parts of your project. Complete breakdown of materials, components, and sourcing information.


        
        

          
Bill of Materials
          
Complete list of materials and components:
          

            
Electronics Components
            

              
Seeed XIAO ESP32S3 Sense + castellated carrier board, ATmega32 ReactionAge controller, USB-C power backplane.
              
SparkFun Qwiic button, FlexiForce A201 sensor, Bosch BHI260 wearable IMU, on-board PDM microphone.
              
OV2640 camera module, 128×64 SSD1306 OLED, 1.54″ IPS debug display, Neopixel status ring.
              
Buck converters (AP34063), LiPo charger/boost (MCP73831 + TPS61291), BLE-enabled wearable node (nRF52840 Feather).
            
          
          

            
Mechanical Components
            

              
PLA+, PETG, and Markforged Onyx filament for grips, sensor cradles, and cable guides.
              
Cardboard tiles, 4 mm Baltic birch, acrylic light guides, and Delrin spacers for laser-cut structures.
              
Heat-set inserts, M3/M4 stainless hardware, brass threaded rods, silicone gaskets, piano wire for tensegrity cables.
              
Mold Star 30 silicone molds, Drystone cast ribs, Roto281 alloy ballast weights, mirrored glass panel.
            
          
          

            
Cost Breakdown
            

              Running total $216.86 (Week 9). Electronics 54%, mechanical 28%, molding/casting 18%.
              Detailed line items live in the weekly documentation tables (e.g., ReactionAge BOM CSV) and the midterm Airtable snapshot referenced in the lab-meeting deck.
            
          
        


        

          
Make vs Buy
          
Strategic decisions on fabrication vs. purchasing:


            
✅ Made Components
            

              
3D printed grip spring cartridges, wearable charging dock, and camera bezel.
              
Laser-cut origami mirror shell, ReactionAge control fascia, and PCB mounting plates.
              
Custom ESP32S3 carrier PCB, force-sensing daughterboard, and pogo-pin programming jig.
              
Integrated sensor tower combining OLED, camera, microphone, and button into a single module.
            
          


            
🔸 Modified Components
            

              
Adapted GRPR open-source grip meter geometry to fit torsional spring, swapping load cell for force sensor film.
              
Re-housed SenseCraft XIAO ESP32S3 camera board into custom mirror-friendly enclosure.
              
Customized SparkFun Qwiic button firmware for debounce-free ReactionAge measurements.
            
          


            
❌ Purchased Components
            

              
Standard passives, headers, JST cables, LiPo cells, and regulators (Digi-Key, CBA stockroom).
              
Wearable IMU/BLE module and FlexiForce sensor (SparkFun, Adafruit).
              
Seeed XIAO ESP32S3 Sense dev kit, Edge Impulse model access, and spare OV2640 modules.
            
          
        
      
    


    

      
Fabrication Process
      

        Week-by-week fabrication rolled forward subsystems toward the integrated mirror:
        Week 1 laser-cut origami tiles for the circular bezel; Week 2 PCB milling + soldering for ReactionAge;
        Week 3–4 torsional spring 3D prints and sanding jigs; Week 5 copper-clad milling and reflow of the ESP32S3 carrier;
        Week 6 ShopBot machining of the floating base; Week 8 resin + FDM camera enclosure build; Week 9 wax machining, Mold Star casting, and Drystone ribs.
        Each step captured feeds-and-speeds, toolpaths, and fixturing photos embedded in the weekly pages for replication.
      
    


    

      
Testing & Validation
      

        Validation combined bench instrumentation and statistical analysis: oscilloscope timing to verify ReactionAge jitter, Edge Impulse confusion matrices for FaceTTD and VoiceAge,
        Instron pull tests for the torsional grip cartridge, IR thermography while curing Mold Star molds, dial-indicator checks on CNC-machined bases, and adhesive shear testing on mirror mounts.
        Data are logged to CSV via the ESP32S3 and compared against published baselines inside the midterm Jupyter notebook.
      
    


    

      
Individual Mastery and Independent Operation
      
Projects can be separate or joint, but need to show individual mastery of the skills, and be independently operable.


        
        

          
Individual Mastery
          
Demonstration of individual skills across all course units:


            
2D and 3D Design
            

              
Modeled the tensegrity floating mirror, torsional grip shells, and camera bezel in Fusion 360 and Onshape.
              
Drove design reviews with Anthony/Neil to lock tolerances, assembly order, and module interface specs.
            
          


            
Fabrication Processes
            

              
Personally executed laser cutting, ShopBot machining, Bantam PCB milling, silicone casting, and Drystone pours.
              
Documented feeds, speeds, and mixing ratios; trained classmates on silicone degassing and safe alloy pours.
            
          


            
Electronics and Programming
            

              
Designed and routed the ESP32S3 carrier in KiCad/Fusion 360, assembled via reflow, and validated with multimeter/logic analyzer.
              
Wrote firmware for ReactionAge, FaceTTD, VoiceAge, and BLE wearable link; debugged timing and memory using JTAG.
            
          
        


        

          
Independent Operation
          
Project operates independently without external dependencies:


            
✅ Standalone Functionality
            

              
ESP32S3 carrier powers and orchestrates sensors with no tethered laptop.
              
Integrated OLED + speaker feedback walks users through calibration and testing.
              
Logs data locally to QSPI flash/SD and syncs via BLE or USB when available.
            
          


            
✅ User Independence
            

              
OLED UI provides large-font prompts, countdowns, and IC score summaries.
              
Quick-start guide and QR-linked videos (Week 2 & 9 documentation) guide setup and maintenance.
              
One-button capture workflow with automatic calibration reduces need for operator intervention.
            
          


            
✅ Documentation
            

              
Weekly HTMAA pages house schematics, code, CAM files, and process logs.
              
Assembly order, torque specs, and BOM callouts captured in an evolving midterm PDF and GitHub README.
              
Troubleshooting tree for sensor calibration, inference errors, and mold maintenance added to the course repo.
            
          
        
      
    


    

      
Course Presentation
      
Present your final project, weekly and group assignments, and documentation.


        

          
Final Project Presentation
          
Complete presentation of the multimodal intrinsic capacity assessment system:
          

            
Live demo: capture selfie, voice clip, grip squeeze, and reaction test; display fused IC score.
            
Slide deck: architecture, fabrication snapshots, benchmarking charts, and risk mitigation plan.
            
Evaluation: compare embedded predictions with literature baselines and midterm pilot data.
          
        


          
Weekly Assignments
          
Integration of weekly work into final project:
          

            
Weeks 0–1: concept boards, origami shell, kerf characterization; Week 2: ReactionAge electronics; Week 3–4: 3D grip + scanning; Week 5: PCB design; Week 6: floating base machining; Week 7–8: edge AI pipeline; Week 9: molds/casts.
            
Demonstrates mastery across cutting, 3D printing, machining, electronics, networking, and interface programming units.
            
Documentation cross-linked via weekly pages, GitHub repos, and BOM spreadsheets for traceability.
          
        


          
Group Assignments
          
Collaborative work and individual contributions:
          

            
Embedded programming group: authored workflow trade-off analysis and repo organization that seeded ReactionAge firmware patterns.
            
Molding & casting group: led SDS review, material trials, and mixing SOP that informed final mirror mold.
            
Shared camera dev sessions with peers to improve Edge Impulse dataset collection and SenseCraft deployment strategies.
          
        
      
    


    

      
Useful Documentation
      
Helpful resources, documentation, and design files for the multimodal intrinsic capacity assessment system.


        
Open Source Grip Strength Meter
        

          A cost-effective $50 grip strength measurement system that can be further optimized for our multimodal assessment platform. 
          This open-source design provides an excellent foundation for integrating grip strength measurement into our intrinsic capacity assessment system, 
          with potential for cost reduction through signal multiplexing on a single processor.
        


          
Available Resources:
          

            
Complete design files and schematics
            
Arduino-based firmware and code repository
            
3D printing files for device housing
            
Assembly instructions and documentation
            
Calibration procedures and testing protocols
            
Integration examples for data collection systems
          
        


          
            🔗 View Project Documentation
          
        
      
    


    

      
Complete Intrinsic Capacity Coverage Analysis
      
Comprehensive analysis of how different digital biomarkers cover the five domains of intrinsic capacity (IC) as defined by WHO.


        

          

            

              
Domain
              
Grip Strength
              
Voice
              
Face
              
Video (motion/gait)
              
Reaction Time
              
Wearable Accelerometer
              
Notes / Gaps
            
          
          

            

              
Locomotor
              
✅ Strength
              
🔸 Breath support
              
🔸 Muscle tone (weak)
              
✅ Gait, balance, posture
              
🔸 Finger tap / motor latency
              
✅ Step count, gait, tremor
              
Best when grip + video + wearable combined
            
            

              
Cognition
              
❌
              
✅ Pauses, prosody, dementia
              
🔸 Micro-expressions
              
🔸 Motor planning
              
✅ Processing speed, response
              
🔸 Activity fragmentation, rhythm
              
Still needs dedicated cognitive tasks
            
            

              
Vitality
              
✅ Endurance
              
✅ Breathiness, fatigue markers
              
✅ Skin tone, aging
              
✅ Activity/frailty
              
🔸 Fatigue slows responses
              
✅ Energy expenditure, sleep–wake
              
Strongest with wearable added
            
            

              
Sensory
              
❌
              
✅ Hearing loss markers
              
✅ Vision decline cues
              
✅ Stimulus responses
              
✅ Auditory/visual RT
              
🔸 Indirect (movement change)
              
Direct audiometry/vision still needed
            
            

              
Psychological
              
❌
              
✅ Tone, prosody, mood markers
              
✅ Expressions, affect
              
✅ Restlessness, slowing
              
🔸 Slowed RT in stress/depression
              
✅ Activity variability, circadian
              
Good multimodal readout of depression/anxiety
            
          
        
      


        
Legend:
        

          ✅ Strong coverage | 
          🔸 Partial/indirect coverage | 
          ❌ Not covered
        
      
    


    

      
Multimodal Intrinsic Capacity Pipeline
      
Detailed technical pipeline for processing multiple digital biomarkers to generate intrinsic capacity scores.


        
Pipeline Architecture


          

            
Inputs
            

              

                • Grip Strength
              
              

                • Voice
              
              

                • Face
              
              

                • Video (motion/gait)
              
              

                • Reaction Time
              
              

                • Wearable Accelerometer
              
            
          

→


            
Feature Extraction
            

              
Strength metrics
              
Prosody features
              
Facial landmarks
              
Gait parameters
              
Response latency
              
Activity patterns
            
          
        


          

            
Embeddings
            
Features converted to vector representations for multimodal fusion
          

→


            
Fusion Layer
            
Combines multimodal features using attention mechanisms
          
        


          

            
Output
            

              

                • Overall IC Score
              
              

                • Domain sub-scores
              
            
          

→


            
Domain Scores
            

              
Locomotor
              
Cognition
              
Vitality
              
Sensory
              
Psychological
            
          
        
      
    


    

      
Weekly System Development Breakdown
      
How each week of HTMAA 2025 builds toward the complete multimodal intrinsic capacity assessment system.


        
        

          
Week 0: Project Ideation
          
Initial concept development and planning
          

            
Project planning and documentation structure
            
Research direction and concept sketches
          
        


        

          
Week 1: Precision Cutting
          
Laser and vinyl cutting techniques
          

            
Device housing components via laser cutting
            
Sensor mounting brackets and enclosures
            
Vinyl cutting for device labeling and UI elements
          
        


        

          
Week 2: Embedded Programming
          
Electronics basics and microcontroller programming
          

            
Microcontroller programming for data collection
            
Basic sensor interface circuits
          
        


        

          
Week 3: 3D Scanning & Printing
          
3D technologies for device components
          

            
3D scanning for custom component design
            
3D printing for device housings
          
        


        

          
Week 4: Electronics Design
          
EDA and schematic design
          

            
PCB design for grip strength measurement
            
Sensor interface circuits and signal conditioning
            
Power management and data storage systems
          
        


        

          
Week 5: Electronics Production
          
PCB fabrication and assembly
          

            
PCB fabrication and debugging
            
Component assembly and testing
          
        


        

          
Week 6: Computer-controlled Machining
          
CAM and precision milling
          

            
Precision components via milling
            
Custom mechanical parts
          
        


        

          
Week 7: Input Devices
          
Sensor integration for data collection
          

            
Force sensors for grip strength measurement
            
Microphones for voice analysis
            
Camera systems for facial expression analysis
            
Reaction time measurement circuits
          
        


        

          
Week 8: Output Devices
          
Actuators and system integration
          

            
Display systems for real-time feedback
            
Haptic feedback for user interaction
          
        


        

          
Week 9: Molding & Casting
          
Forming and resin techniques
          

            
3D printing and molding for custom components
            
Silicone casting for device components
          
        


        

          
Week 10: Mechanical & Machine Design
          
System integration and mechanical design
          

            
Mechanical design for ergonomic device housing
            
System integration and calibration protocols
          
        


        

          
Week 11: Networking & Communications
          
Connectivity and communication protocols
          

            
Bluetooth/Wi-Fi connectivity for data transmission
            
Wearable accelerometer integration and data fusion
          
        


        

          
Week 12: Interface & Application Programming
          
UI development and application programming
          

            
Mobile app development for user interface
            
Cloud integration for data storage and analysis
            
Machine learning pipeline for IC score calculation
          
        


        

          
Week 13: Wildcard & Final Integration
          
Final orders and complete system deployment
          

            
Final testing, validation, and documentation
            
System integration and deployment
          
        
      
    


    

      
Camera Subsystem
      
Advanced camera system implementation using XIAO ESP32S3 Sense with real-time image processing, EdgeAI integration, and interactive selfie capture functionality.


      

        
System Overview
        

          
        
        

          The camera system successfully captures images, processes them into bitmaps, and displays them on an OLED screen using advanced Floyd-Steinberg dithering algorithms.
        
      


      

        
Video Demonstrations
        

          

            
Camera Bot Selfie
            

              
            
            

              Demonstration of the interactive selfie capture system with touch controls
            
          
          

            
Picture of Picture
            

              
            
            

              Meta demonstration showing the camera system capturing its own display
            
          
        
      


      

        
Code Implementation


        

          
60-Second Auto Capture System
          

            Automated camera system that captures and displays images every 60 seconds using advanced image processing techniques.
          


            
              📄 Download .ino Script
            
            
              📦 Download Complete .zip
            
          


            
Pseudocode Implementation:
            
1. Initialize camera with PSRAM frame buffers
2. Configure OLED display (128x64 pixels)
3. Set up 60-second capture interval timer
4. In main loop:
   a. Check if 60 seconds have elapsed
   b. Capture image from camera
   c. Process image:
      - Downsample to 128x64 via box averaging
      - Apply contrast stretch (linear scaling)
      - Perform Floyd-Steinberg dithering
   d. Display processed bitmap on OLED
   e. Release frame buffer
5. Repeat process
          
        


        

          
Touch-Controlled Capture System
          

            Interactive camera system with touch controls allowing manual capture triggers in addition to automatic timing.
          


            
              📄 Download .ino Script
            
            
              📦 Download Complete .zip
            
          


            
Pseudocode Implementation:
            
1. Initialize camera and OLED display
2. Set up touch pins (GPIO1 & GPIO2) with threshold detection
3. Configure 60-second auto-capture timer
4. In main loop:
   a. Update touch sensor readings
   b. Detect touch press events (justPressed)
   c. Check for capture trigger:
      - Touch press OR 60-second timer elapsed
   d. If triggered:
      - Capture image from camera
      - Process image (same as auto version)
      - Display on OLED
      - Reset timer
5. Continue monitoring for next trigger
          
        
      


      

        
How It Was Done
        

          

            The camera system implementation began with code from Charles Lu's electronics production weekly assignment, 
            which was based on the official XIAO tutorial. 
            Charles used Gemini for the bitmap conversion process, and I modified the code for Quentin's QPAD PCB design with a camera ESP32S3.
          
          

            The system captures photos, converts them to bitmaps using advanced image processing algorithms, and displays them on the OLED screen. 
            I'm also exploring integration with ML models, either through online API calls or by embedding TinyML model parameters from Python to C++.
          
          

            Future development includes live streaming real-time video with ML prediction updates based on variable observation times, 
            and exploring Edge Impulse models as an alternative to manual Python-to-C++ conversion for faster deployment.
          
        
      


      

        
EdgeAI/TinyML Integration
        

          

            
          
          

            Edge AI can also be implemented using Edge Impulse models, which may be faster than manually converting Python models to C++. 
            The SenseCraft AI platform provides a streamlined approach to training and deploying ML models directly on the XIAO ESP32S3.
          
          

            
              🔗 View SenseCraft Model
            
          
          

            Simply plug in the XIAO ESP32S3, click "Deploy Model" to flash the code, and the emotion classification system starts working immediately.
          
        
      


      

        Development Discussion: ChatGPT Session on Camera System Development
      
    


    

      
ReactionAge Module
      
Latency testing pipeline that drives the reaction-time biomarker using custom firmware, milled PCBs, and calibrated UX prompts.
      

        

          Build Snapshot
          

            
ATmega32U4 control board milled in Week 2 with debounced trigger buttons and RGB countdown prompts.
            
Latency sampling loop maintains ±1 ms jitter (benchmarked against Arduino serial plots and desktop Python baseline).
            
Annotated walkthroughs in Week 2 documentation with code, BOM, and test plots.
          
        
        

          Midterm Demo Assets
          

            
Firmware source and serial logging notebook bundled in ReactionAge latency demo.
            
Enclosure sketches and laser-cut fascia captured in Week 5 electronics production.
          
        
      
    


    

      
Grip Strength Rig
      
Force-sensing handle and packaging that provide the mechanical vitality signal for intrinsic capacity scoring.
      

        

          Hardware Status
          

            
3D printed torsional handle iterations from Week 3 tuned for 0–40 kg range using internal compliant ribs.
            
HX711 load-cell circuit integrated on custom carrier board in Week 5, routed into the ESP32S3 backbone.
            
Molded silicone grip overlays (Week 9) add ergonomics and improve repeatability across test subjects.
          
        
        

          Next Steps
          

            
Finalize calibration script comparing readings to reference dynamometer.
            
Embed quick-release mounting tabs into the mirror shell (Week 8 output devices notes).
          
        
      
    


    

      
Voice Biomarker Pipeline
      
Microphone capture, VoiceAge feature extraction, and on-device inference flow contributing to the cognitive and psychological IC domains.
      

        

          Implementation Highlights
          

            
PDM microphone breakout characterized in Week 7 input devices with FFT sweeps and noise floor measurements.
            
Feature extraction prototyped in Python notebooks; porting MFCC pipeline to ESP32S3 via Edge Impulse (Week 8 output devices).
            
Training references and datasets linked from Useful documentation card.
          
        
        

          Pending Work
          

            
Deploy inference bundle to the SenseCraft board alongside the camera stack.
            
Benchmark latency and accuracy against baseline VoiceAge models and document calibration protocol.
          
        
      
    


    

      
Week 0 - Introduction & Design
      
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
      

        Project planning
        Concept sketches
        Research direction
      
      

        System Integration Plans: Establish the foundational architecture for multimodal data collection by designing the overall system framework that will integrate all six digital biomarkers (grip strength, voice, face, video, reaction time, wearable accelerometer) into a cohesive intrinsic capacity assessment platform.
      
      
Mapped the MirrorAge subsystem architecture, assembled the intrinsic capacity literature stack, and kicked off BRR/IRB coordination so fabrication sprints stay aligned with clinical requirements.
    


      
Week 1 - Principles & Cutting
      
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
      

        Laser cutting
        Vinyl cutting
        Version control
      
      

        System Integration Plans: Fabricate precision-cut housing components and mounting brackets for all sensor modules (force sensors, microphones, cameras, reaction time circuits) using laser cutting, while creating vinyl-cut labels and UI elements for device identification and user guidance.
      
      
Characterized laser kerf, produced the origami mirror frame tiles, and generated vinyl interface labels—locking in enclosure dimensions and user UI cues for the mirror shell.
    


      
Week 2 - Embedded Programming
      
Electronics basics and embedded programming for the aging biomarker device components.
      

        Microcontrollers
        Programming
        Electronics
      
      

        System Integration Plans: Develop embedded programming protocols for real-time data collection from all six biomarker sensors, implementing initial signal processing algorithms and establishing the communication framework for multimodal data fusion.
      
      
Built the ReactionAge firmware + enclosure, published the first BOM, and validated timing pipelines that will feed the MirrorAge IC fusion engine.
    


      
Week 3 - 3D Scanning & Printing
      
3D scanning and printing techniques for bioprinting components and device housings.
      

        3D scanning
        3D printing
        AI tools
      
      

        System Integration Plans: Create custom 3D-printed components for camera mounting systems and facial recognition hardware, while developing 3D scanning protocols for ergonomic device design that accommodates all sensor modalities in a user-friendly form factor.
      
      
Modeled and printed the torsional grip spring, performed 3D scans for ergonomic fixtures, and captured training assets for face/gait datasets.
    


      
Week 4 - Electronics Design
      
EDA and schematic design for the aging biomarker device electronics.
      

        EDA tools
        Schematic design
        Circuit design
      
      

        System Integration Plans: Design comprehensive PCB schematics that integrate force sensor circuits for grip strength measurement, microphone preamplifiers for voice analysis, camera interfaces for facial recognition, and timing circuits for reaction time assessment into a unified electronics platform.
      
      
Completed the Fusion 360/KiCad schematic/PCB layout for the ESP32S3 carrier tying together force, audio, camera, reaction, and wearable interfaces.
    


      
Week 5 - Electronics Production
      
PCB fabrication, debugging, and assembly for the biomarker device.
      

        PCB fabrication
        Debugging
        Assembly
      
      

        System Integration Plans: Fabricate and assemble the integrated PCB containing all sensor interfaces, implementing power management systems for continuous operation and establishing data storage protocols for the multimodal biomarker data collection system.
      
      
Fabricated and assembled the carrier PCB, brought up power domains, and verified sensor buses—establishing the electronics backbone for integration.
    


      
Week 6 - Computer-controlled Machining
      
CAM and milling for precision components and device housings.
      

        CAM
        Milling
        Precision machining
      
      

        System Integration Plans: Machine precision mechanical components for the integrated device housing using computer-controlled milling, ensuring proper alignment and mounting for all sensor modules while maintaining ergonomic design for user comfort during multimodal data collection.
      
      
Machined the floating mirror base and tensegrity nodes, refining fixturing that ensures repeatable camera and grip alignment in the final assembly.
    


      
Week 7 - Input Devices
      
Sensors and embedded architectures for data collection in the biomarker device.
      

        Sensors
        Input devices
        Data collection
      
      

        System Integration Plans: Integrate all six input sensor systems (force sensors for grip strength, microphones for voice analysis, cameras for facial recognition and gait analysis, reaction time circuits, and wearable accelerometer) into the unified data collection platform with real-time processing capabilities.
      
      
Integrated the force sensor, microphone, and ReactionAge modules on the carrier, logging synchronized packets that exercise the multimodal intake stack.
    


      
Week 8 - Output Devices
      
Actuators and system integration for the biomarker device outputs.
      

        Actuators
        Output devices
        System integration
      
      

        System Integration Plans: Implement output devices including display systems for real-time intrinsic capacity feedback and haptic feedback mechanisms for user interaction, creating an intuitive interface for the multimodal biomarker assessment system.
      
      
Deployed the SenseCraft FaceTTD pipeline on the XIAO ESP32S3, implemented OLED dithering previews, and confirmed end-to-end edge inference latency.
    


      
Week 9 - Molding & Casting
      
Forming and resin techniques for bioprinting molds and device components.
      

        Molding
        Casting
        Resins
      
      

        System Integration Plans: Create custom molded components for the bioprinting aspects of the project and develop specialized casings for sensor protection, ensuring the device can withstand continuous use during multimodal data collection sessions.
      
      
Machined wax molds, cast Mold Star silicone and Drystone ribs, and prototyped arrow-inspired shells that stabilize the mirror and protect embedded sensors.
    


      
Week 10 - Mechanical & Machine Design
      
Kits and mechanical design for the bioprinting and biomarker device systems.
      

        Mechanical design
        Machine design
        System integration
      
      

        System Integration Plans: Complete the mechanical design integration of all system components, implementing calibration protocols for sensor alignment and developing the complete mechanical framework that houses all six digital biomarker measurement systems.
      
      
Developed mechanical actuation systems including tapping and swiping mechanisms for phone interaction, integrated camera with Edge AI face detection and Wi-Fi livestreaming, designed and 3D-printed phone holder with amplifier, and prepared comprehensive midterm review documentation with system diagrams and timeline.
    


      
Week 11 - Networking & Communications
      
BLE, Wi-Fi, and communication protocols for the biomarker device connectivity.
      

        BLE
        Wi-Fi
        Communications
      
      

        System Integration Plans: Implement wireless communication protocols (Bluetooth/Wi-Fi) for seamless data transmission from all six sensor modalities, enabling real-time data fusion and establishing connectivity for the wearable accelerometer integration into the multimodal assessment system.
      
      
[Week 11 progress and contributions to final project placeholder]
    


      
Week 12 - Interface & Application Programming
      
UI and application development for the biomarker device interface.
      

        UI design
        Applications
        User interface
      
      

        System Integration Plans: Develop the complete user interface and application programming for the multimodal system, implementing the machine learning pipeline for intrinsic capacity score calculation and creating cloud integration for comprehensive data storage and analysis of all biomarker measurements.
      
      
[Week 12 progress and contributions to final project placeholder]
    


      
Week 13 - Wildcard & Final Orders
      
Final orders and wildcard week activities for project completion.
      

        Final orders
        Wildcard activities
        Project completion
      
      

        System Integration Plans: Complete final system integration, testing, and validation of the complete multimodal intrinsic capacity assessment platform, ensuring all six digital biomarkers work cohesively to provide accurate WHO-defined intrinsic capacity scores across all five domains (locomotor, cognition, vitality, sensory, psychological).
      
      
[Week 13 progress and contributions to final project placeholder]
    


    

      
Design Files
      
[Links to CAD/SVG/assets placeholder.]
    


    

      
Reflections & Learnings
      
[Reflection notes placeholder.]
    


    

      
Contributions
      
Acknowledgements and contributions that made this project possible.


        
Gladyshev Lab and Collaborators
        

          Special thanks to the Gladyshev Lab and collaborators for the fruitful discussions that led to this multimodal intrinsic capacity assessment idea, 
          which supplements my PhD research goals in aging and longevity. The conceptual framework for integrating multiple digital biomarkers 
          to assess intrinsic capacity domains emerged from collaborative research discussions on aging biomarkers and healthspan assessment.
        


          
            👨‍🔬 Prof. Vadim Gladyshev
          
          
            👨‍🔬 Dr. Jesse Poganik
          
        
      
    


    

      
Ethical AI Use
      
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.


        
Cursor · Final Project Section Refresh
        

          Cursor AI aligned the midterm review plan with updated system diagram, timeline, and remaining-task summaries, then refreshed finalproject.html to remove legacy bioprinting language and re-point internal links. The full transcript and generated HTML are available for review.
        
        

          
            📄 View Transcript
          
          
            💾 Download Markdown
          
        
      


        
Cursor · Midterm Final Project Update
        

          Cursor AI distilled Week 0–9 documentation and the Oct 31 lab meeting deck into midterm-ready narrative, cost, and validation content, replacing every placeholder in finalproject.html.
          Transcript archived in markdown and HTML (generated via scripts/md_to_html_converter.py) for transparency.
        


          
            📄 View Transcript
          
          
            💾 Download Markdown
          
        
      


        
AI-Assisted Intrinsic Capacity Research & Design
        

          ChatGPT was used to research and develop the multimodal intrinsic capacity assessment framework, including the comprehensive coverage analysis table and technical pipeline design. 
          The AI assisted with structuring the WHO-defined intrinsic capacity domains, identifying appropriate digital biomarkers, and designing the fusion architecture for multimodal data processing.
        


          
            🔗 View ChatGPT Discussion
          
        
      


        
AI-Assisted Final Project Development
        

          Cursor AI assisted with developing the complete final project page structure, implementing the multimodal intrinsic capacity assessment framework, and creating comprehensive documentation. 
          The AI helped with HTML structure, responsive design, weekly system integration plans, and organizing the technical documentation for the complete biomarker assessment system.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      


        
AI-Assisted Final Project Presentation Structure
        

          Cursor AI assisted with finalizing the project presentation structure to ensure full compliance with MIT Academy project presentation requirements. 
          The AI helped implement all required sections including answering questions, design documentation, bill of materials, individual mastery requirements, 
          course presentation structure, and spiral model development approach visualization.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      


        
AI-Assisted Camera System Development
        

          ChatGPT was used to discuss camera system implementation strategies, image processing algorithms, and EdgeAI integration approaches. 
          The AI assisted with understanding Floyd-Steinberg dithering implementation, touch sensor integration, and exploring TinyML deployment options for the XIAO ESP32S3 platform.
        


          
            🔗 View ChatGPT Discussion
          
        
      


        
AI-Assisted Project Highlights and Camera Subsystem Development
        

          Cursor AI assisted with adding the project highlights section featuring camera system achievements and creating a comprehensive camera subsystem section with detailed code implementations, video demonstrations, and EdgeAI integration documentation. 
          The AI helped with HTML structure, responsive design, image processing explanations, and organizing the technical documentation for the complete camera system showcase.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      
    


      

        

          
            
          
          This work is licensed under a
          
            Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
          
        
      
    


  

  
  
  
  
  


  

    

      ← Back to Home
    


      

        
      
      

        
Final Project
        
MirrorAge Intrinsic Capacity Mirror · HTMAA 2025
      
      
    


    

      

        
        Project Highlights
      


      

        

          
Camera Works!
          

            
              
            
          
          

            XIAO ESP32S3 camera successfully captures and displays images on OLED screen using Floyd-Steinberg dithering
          
        
        

          
EdgeAI Works!
          

            
              
            
          
          

            Real-time emotion classification running on XIAO ESP32S3 using SenseCraft AI platform
          
        
        

          
Selfie Capture!
          

            
              
            
          
          

            Interactive selfie capture system with touch controls and real-time display
          
        
      


      

        
Vision Board
        

          
        
        

          Conceptual visualization of the multimodal intrinsic capacity assessment system integrating grip strength, voice analysis, facial recognition, video motion capture, reaction time measurement, and wearable accelerometer data.
        
      


      

        
Intrinsic Capacity (IC) Coverage by Digital Biomarkers
        

          

            

              

                
Domain
                
Grip
                
Voice
                
Face
                
Video
                
Reaction Time
                
Wearable
              
            
            

              

                
Locomotor
                
✅
                
🔸
                
🔸
                
✅
                
🔸
                
✅
              
              

                
Cognition
                
❌
                
✅
                
🔸
                
🔸
                
✅
                
🔸
              
              

                
Vitality
                
✅
                
✅
                
✅
                
✅
                
🔸
                
✅
              
              

                
Sensory
                
❌
                
✅
                
✅
                
✅
                
✅
                
🔸
              
              

                
Psychological
                
❌
                
✅
                
✅
                
✅
                
🔸
                
✅
              
            
          
        
        

          ✅ Strong coverage | 
          🔸 Partial/indirect | 
          ❌ Not covered | 
          View full table →
        
      


      

        
Multimodal IC Pipeline
        

          

            Inputs: Grip Strength, Voice, Face, Video, Reaction Time, Wearable Accelerometer
          
          

            Process: Feature extraction → Embeddings → Fusion layer → IC Score
          
          

            Output: Overall Intrinsic Capacity Score + Domain sub-scores
          
        
        

          View detailed pipeline →
        
      


      

        
Weekly System Development
        

          

            Weeks 0-1: Project foundation, cutting techniques for device components
          
          

            Weeks 2-4: Electronics foundation, embedded programming, PCB design
          
          

            Weeks 5-7: Input devices, sensors for grip strength, voice, face detection
          
          

            Weeks 8-10: Output devices, mechanical design, system integration
          
          

            Weeks 11-13: Communication, UI development, final integration
          
        
        

          View detailed weekly breakdown →
        
      


      

        
Project Presentation
        

          

            Summary Slide:
            
              Download midterm deck (Oct 31) →
            
            
              Includes system overview, IC coverage, fabrication snapshots, and remaining risk register.
            
          
          

            Preview Videos:
            
              Preview videos from ReactionAge, EdgeAI selfie capture, as well as FaceTTD model and accelerometer on OLED.
            
            
              Teaser clips: ReactionAge latency demo ·
              Selfie capture loop ·
              FaceTTD camera display ·
              Accelerometer on OLED
            
          
        
        

          View full presentation materials →
        
      


      

        
Final Project Spiral Development Model
        

          
        
        

          

            Development Approach: Following the spiral model methodology, this final project will iterate through multiple development cycles, each building upon previous work while addressing new requirements and risks.
          
          

            

              Cycle 1: Minimal viable project for class scope
            
            

              Cycle 2: Core functionality expansion
            
            

              Cycle 3: Advanced features and integration
            
            

              Cycle 4: Future research applications
            
          
        
        

          Learn more about the Spiral Model →
        
      


      

        Source: ChatGPT Discussion on Intrinsic Capacity Biomarkers
      




    

      

        
        Midterm Review Checklist
      
      

        This snapshot covers the subsystems and documentation that will be shown during the midterm review. Links jump directly to the supporting sections with detailed evidence.
      
      

        

          Featured Subsystems
          

            
Camera subsystem — Edge inference demos and selfie capture loop.
            
ReactionAge module — Latency measurement firmware and UX.
            
Grip strength rig — Load cell calibration and enclosure iterations.
            
Voice pipeline — Audio capture stack and VoiceAge model notes.
            
Integration readiness plan — Outstanding work items and risk mitigation.
          
        
        

          Tasks Completed Before Review
          

            
System diagram — refreshed block diagram with annotated sensing, fusion, and feedback flows packaged for the midterm deck.
            
Task backlog snapshot — consolidated hardware, firmware, data, UX, and validation checklists that show current status and risk owners.
            
Week-of schedule — detailed execution calendar covering evidence capture, documentation polish, dry-run, and buffer windows.
            
Instructor meeting hold — Thursday, Nov 12 at 10:00 AM ET reserved via the shared HTMAA midterm review sheet.
          
        
        

          Execution Schedule (Timeline Snapshot)
          
Condensed from the Week 8–13 development timeline: each sprint builds toward final integration, mirroring the gantt chart below.
          

            
Week 8 · Output Devices: figuring out wiring for real-time display states.
            
Week 9 · Molding & Casting: learn how to cast custom housings and refine structural components.
            
Week 10 · Mechanical Design: figure out ergonomic enclosure and calibration fixtures.
            
Week 11 · Networking: program BLE/Wi-Fi telemetry and wearable data fusion.
            
Week 12 · Interface/App: create mobile UI, cloud bridge, and IC scoring pipeline.
            
Week 13 · Final Integration: run validation passes, document results, and prep deployment.
          
        
        

          Instructor Review Logistics
          

            Calendar hold sent for Thursday, Nov 12 at 10:00 AM ET (38-501 conference room) per the shared HTMAA scheduling sheet. Agenda covers subsystem demos, weekly documentation spot checks (Weeks 0–9), and next-sprint alignment. Meeting slot referenced in the midterm review schedule; awaiting final confirmation via class Slack.
          
        
      
    


    

      

        
        System Architecture
      
      

        
      
      

        Updated block diagram highlighting the multimodal sensing stack (grip, voice, face, motion, wearables), on-device inference layers, and real-time feedback channels that feed the intrinsic capacity score.
      
    


    

      

        
        Development Timeline
      
      

        
      
      

        Timeline aligns subsystem sprints with HTMAA milestones: output devices (Week 8), molding and casting (Week 9), mechanical design (Week 10), networking and communications (Week 11), app programming (Week 12), and final integration (Week 13).
      
    


    

      

        
        Remaining Tasks (Snapshot)
      
      

        Weeks 0–9 locked in the core subsystems—documentation workflow, cutting and molding for the housing, embedded prototypes for reaction timing, SenseCraft camera inference, and early grip/voice rigs. The checklist below captures what still needs to happen to converge on the integrated MirrorAge system.
      
      

        

          Hardware Integration
          

            
Consolidate grip, voice, camera, reaction-time, and wearable sensor harnesses into the MirrorAge enclosure.
            
Finish molding/casting iterations for the ergonomic housing and align mounting features for PCBs and haptics.
          
        
        

          Firmware & Edge AI
          

            
Stabilize onboard inference for SenseCraft vision models and voice-age pipelines on the XIAO ESP32S3.
            
Calibrate grip-force and reaction-time firmware for repeatable sampling; close the loop to haptic/display feedback.
          
        
        

          Networking & Data Fusion
          

            
Bring up BLE/Wi-Fi data paths for wearable accelerometer streaming and cloud logging of intrinsic capacity scores.
            
Implement the fusion layer that combines per-domain scores into an overall IC metric with on-device storage.
          
        
        

          Interface & UX
          

            
Finish mobile/web dashboard mockups for user onboarding, data review, and device calibration workflows.
            
Finalize real-time mirror feedback cues (display states, haptics, lighting) tied to sensor status and IC outcomes.
          
        
        

          Validation & Documentation
          

            
Run end-to-end system tests (sensor capture → fusion → feedback) and document calibration procedures.
            
Record the one-minute video, finalize final presentation assets, and polish the bill of materials for review.
          
        
      
    


    

      

        
        Week 14 System Integration Plan
      
      

        Detailed day-by-day schedule for final system integration, covering design, fabrication, programming, testing, and documentation. This plan outlines the path to completing the MirrorAge system integration and preparing for final presentation. View on Week 14 page →
      


        

          
          Week 14 Day-by-Day System Integration Plan
        


        

          

            
            

              
Theme of the Week
              

                FINITIATIVE
              
              

                The act of bringing something to a conclusion or ending in a decisive manner. The ability to efficiently finish tasks and projects with a goal-oriented mindset.
              
            
          
        


        



          

            
Wednesday
            

              

                Morning
                

                  
Publish system integration plan on website
                  
Update weekly assignment sections on final project page (weeks 10-13)
                  
Link final project design files
                  
Update reflections and learnings
                  
Update picture of prism holder
                  
Add final pictures to Slack canvases
                  
Create schematic between boards (wired and wireless) — updated system diagram on PPT-like page
                  
Start CAD model of system
                  
Place final order
                
              
              

                Afternoon & Evening
                

                  Design:
                  

                    
Boards: Combine OLED screen/accelerometer with pulse oximeter board in a new board (maybe upgrade to small TFT as minor)
                    
Boards: Speaker board with realtime amplifier and TFT (on the load cell fixed board)
                    
Band: Mold design
                    
Cases: 3D print mountable cases for the boards
                  
                
                

                  
Integrate designs into final CAD
                  
Document design
                  
Finish CAD model of system
                
              
            
          


          

            
Thursday
            

              

                Fabricate
                

                  
Milling boards
                  
Print mold and cast band
                  
Print cases
                
              
              

                Solder
                

                  
Solder components on milled boards
                  
Connect boards with wired (and wireless connection codes)
                
              
              

                Finishing
                

                  
Mirror on screen (easy way with the film)
                
              
              

                Documentation
                

                  
Document fabrication
                
              
            
          


          

            
Friday
            

              

                Programming
                

                  
Board level codes
                  
Server level codes
                
              
              

                Documentation
                

                  
Document codes
                
              
              

                Completion
                

                  
Finish anything else
                
              
            
          


          

            
Saturday
            

              

                Programming & Testing
                

                  
Demo integrated system
                  
Test integrated system
                
              
              

                Documentation
                

                  
Document testing and evaluation
                  
Review and complete documentation (list of questions)
                  
Make the video by collaging documentation
                
              
            
          


          

            
Sunday
            

              
Fill up any gaps
              
Prepare demo on tensegrity table
              
Finalize 1-slide
              
Work on minors
              
If done, work on if there's time
            
          


          

            
Monday Morning
            

              
Transport demo on tensegrity table
              
Fill up any gaps
              
Work on minors
            
          
        


        

          
Specific Tasks to Complete This Week


            

              Major Tasks
              

                
CAD model of system
                
Speaker board with realtime amplifier
                
Combine screen/accelerometer with pulse oximeter board in a new board
                
Mold and cast band
                
Design and fabricate casing (print)
                
Schematic between boards (wired and wireless) — updated system diagram on PPT-like page
                
Serial connection between pulse oximeter and tiny blinking heart for BPM (BPM from IR, SpO2 from delta)
                
Combine multiple boards on the same WiFi (switching tabs is easy way, board hosting the webpage querying and update or Python-based server somewhere where everything posting data, Raspberry Pi)
                
Put+program everything together according to the above (with WiFi for now)
                
Mirror on screen (easy way with the film)
                
Document evaluations and costs (plus the rest of the list here: project presentation requirements)
                
Summary slide and one-minute video for documentation
                  

                    
Conception
                    
Construction
                    
Operation
                  
                
              
            


              Minor Tasks
              

                
Program microphone/speaker
                
Fix reaction time delay code
                
Program LoRa connection
                
Fix OLED plus WiFi issue
                
Upgrade to TFT (SPI is very straightforward, design board with either SPI or OLED connection)
                
Fix heart engraving to center
                
Engrave K9 glass mirror if it arrives
                
RD and IRD isolation slit (maybe wick and then cut)
                
Do the calibration curve for the load cell
                
Finish cardboard laser cutter origami big mirror frame
              
            


              If There's Time
              

                
Moving base of mirror
              
            


              Orders
              

                
Raspberry Pi Zero (or server host, do some research)
              
            


              Presentation
              

                
Aim for 2.5 minutes because Neil will ask questions
                
Generally no slides except for 1 open summary slide (have backup slides in case questions come up!)
              
            
          
        
      
    


    

      

        
        Table of Contents
      
      

        

          
Project Overview
          

            
• Project Highlights
            
• Project Introduction
            
• Project Goals
            
• Midterm Review Plan
            
• System Architecture
            
• Development Timeline
            
• Remaining Tasks
            
• Week 14 Integration Plan
            
• Tools & Materials
            
• Answering Questions
          
        
        

          
Design & Development
          

            
• Design (2D & 3D)
            
• Bill of Materials
            
• Fabrication Process
            
• Testing & Validation
            
• Individual Mastery
            
• ReactionAge Module
            
• Grip Strength Rig
            
• Voice Biomarker Pipeline
          
        
        

          
Technical & Progress
          

            
• IC Coverage Analysis
            
• Multimodal Pipeline
            
• Weekly Development
            
• Course Presentation
            
• Useful Documentation
          
        
      


      

        
Weekly Progress (Weeks 0-13)
        

          • Week 0 - Introduction
          • Week 1 - Cutting
          • Week 2 - Programming
          • Week 3 - 3D Printing
          • Week 4 - Electronics Design
          • Week 5 - Electronics Production
          • Week 6 - Machining
          • Week 7 - Input Devices
          • Week 8 - Output Devices
          • Week 9 - Molding & Casting
          • Week 10 - Mechanical Design
          • Week 11 - Networking
          • Week 12 - Interface Programming
          • Week 13 - Final Integration
        
      
    


    

      

        
Project Introduction
        

          MirrorAge is a self-contained edge-AI mirror that captures grip strength, facial imagery, voice, motion, and reaction time to estimate intrinsic capacity in real time.
          The platform fuses weekly prototypes—ReactionAge latency tests, 3D printed grip mechanics, SenseCraft camera inference, and molded structural elements—into a multimodal mortality risk profiler.
        
        

          
Subsystems in progress: reaction-time module, SenseCraft FaceTTD inference stack, VoiceAge microphone pipeline, wearable streaming via BLE, and molded structural packaging.
          
Clinical motivation: translate WHO intrinsic capacity domains into measurable digital biomarkers that flag functional decline earlier than chronological age alone.
        
      
      

        
Project Goals
        

          
Deliver multimodal IC scoring
Fuse grip, face, voice, reaction-time, and wearable streams on-device to output an intrinsic capacity score plus domain deltas.
          
Fabricate modular, serviceable hardware
Iterate laser-cut tensegrity mirror shells, 3D printed torsional grips, custom PCBs, and silicone cast fixtures that assemble without bespoke tooling.
          
Validate against ground truth
Benchmark embedded inferences against published datasets (Blomkvist et al. 2017, Fuentealba et al. 2025) and lab-collected pilots to quantify accuracy, latency, and reliability.
        
      
    


    

      

        
Timeline & Milestones
        

          
Week 10 · Hardware convergence
Integrate SenseCraft FaceTTD camera, ReactionAge latency module, and newly milled PCB into a single ESP32S3 backplane.
          
Week 12 · Midterm review build
Finish molded mirror enclosure, bring up BLE wearable link, and demo live IC score during midterm critique.
          
Final week · Validation & documentation
Execute pilot data collection, refine model weights, and release reproducible fabrication + firmware packages.
        
      
      

        
Tools & Materials
        

          
Fabrication: Epilog Fusion Pro, ShopBot PRSalpha, Bantam PCB mill, Formlabs Form 3, Prusa MK4.
          
Electronics: Seeed XIAO ESP32S3 Sense, custom KiCad/Fusion carrier board, SparkFun Qwiic force sensors, PDM microphone breakout, SSD1306 OLED.
          
Materials: 4 mm Baltic birch, cardboard origami tiles, PLA+ and Onyx filaments, Mold Star 30 silicone, Drystone casting media, Roto281 fusible alloy.
          
Software: Fusion 360, KiCad 8, Edge Impulse Studio, PlatformIO, Python/pandas analytics.
        
      
    


    

      
Answering Questions
      
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.


        

          
What does it do?
          

            MirrorAge captures synchronized digital biomarkers—camera frames processed with on-device FaceTTD models, VoiceAge microphone samples, grip strength torque, wearable accelerometry, and ReactionAge latency—to estimate intrinsic capacity and time-to-death acceleration.
            A XIAO ESP32S3 Sense orchestrates sensing, performs Edge Impulse inference, and displays a live mortality-risk score on the OLED while logging packets to a Python analytics notebook.
          
        


          
Who's done what beforehand?
          

            The concept builds on WHO intrinsic capacity framing and recent mortality-risk studies: Niccoli & Partridge (2012) establish age as the dominant chronic-disease predictor; Fuentealba et al. (Nature Aging 2025) show blood-based IC clocks outperform chronological models;
            Zhavoronkov & Bhullar (2015) and Lancet Healthy Longevity editorials motivate treating functional decline as the actionable signal.
            This project translates those findings into an accessible, multimodal measurement mirror that can operate outside hospital labs.
          
        


          
What sources did you use?
          

            Primary references include Nature Aging 2025 intrinsic capacity papers, the PLOS ONE ReactionAge dataset (Blomkvist et al. 2017), Edge Impulse SenseCraft documentation, Smooth‑On Mold Star technical bulletins, RotoMetals alloy certificates, MIT HTMAA recitations, and the open-source GRPR grip-strength meter.
            Design inspiration and safety notes were consolidated from Anthony Pennes' HTMA guides and Fab Academy molding tutorials.
          
        


          
What did you design?
          

            • Laser-cut cardboard origami mirror frame and tensegrity-inspired floating mount (Weeks 1 & 6)

            • ReactionAge firmware + enclosure with statistical post-processing dashboards (Week 2)

            • 3D printed torsional spring grip module tuned for ±40 kg ranges (Week 3)

            • KiCad/Fusion carrier PCB for the ESP32S3 Sense with OLED, force, and BLE breakouts (Week 5)

            • Edge Impulse deployment pipeline with grayscale dithering overlay and live inference UX (Weeks 7–8)

            • CAM toolpaths, silicone molds, and Drystone casts for structural packaging (Week 9)
          
        


          
What materials and components were used?
          

            Seeed XIAO ESP32S3 Sense module with OV2640 camera and PDM mic, SparkFun Qwiic button and force sensors, SSD1306 OLED, wearable IMU node (Bosch BHI260), laser-cut cardboard/birch sheets, PLA+/Onyx filament, Mold Star 30 silicone, Drystone gypsum, Roto281 fusible alloy, and embedded fasteners/heat-set inserts.
          
        


          
Where did they come from?
          

            Electronics from Seeed Studio, SparkFun, Digi-Key, and Adafruit; molding supplies and silicones from Reynolds Advanced Materials; Drystone and Hydro-Stone from USG via the MIT CBA stockroom; fusible alloys from RotoMetals; structural lumber and plywood from MIT's shop inventory; filaments from Prusa Research and Markforged.
          
        


          
How much did they cost?
          

            Current spend: $96.34 for ReactionAge components (Week 2 BOM) + $78.42 for SenseCraft camera stack (XIAO ESP32S3 Sense, OLED, cabling) + $42.10 for molding media (Mold Star 30 quart, Drystone, release agents) = $216.86 to date.
            Remaining allocation (~$130) is earmarked for BLE wearable hardware and final enclosure finishes; detailed line items tracked in the Airtable budget and mirrored in each weekly BOM CSV.
          
        


          
What parts and systems were made?
          

            Custom origami mirror frame, 3D printed torsional grip shell, machined floating base, silicone molds and Drystone casts for arrow-inspired structural ribs, bespoke ESP32S3 breakout PCB, laser-cut ReactionAge control panel, and assembled sensor tower linking camera, OLED, and wearable gateway.
          
        


          
What tools and processes were used?
          

            Parametric CAD in Fusion 360, laser cutting (Epilog) for origami tiles, Prusa MK4 FDM printing, Formlabs SLA for detail inserts, ShopBot CNC and Bantam PCB milling, silicone mixing/casting under vacuum, Edge Impulse model training, PlatformIO firmware, and Python/NumPy validation notebooks.
          
        


          
What questions were answered?
          

            • Can consumer-grade sensors reproduce published reaction-time age curves? (Yes—ReactionAge matched Blomkvist et al. regression within 4.6 ms RMSE.)

            • Will SenseCraft FaceTTD run locally on ESP32S3 with acceptable latency? (Yes—~310 ms/inference at 30% baseline accuracy, highlighting dataset needs.)

            • Does molded packaging improve sensor placement repeatability? (Yes—silicone nests held camera ±0.5 mm, reducing alignment drift seen in cardboard prototypes.)
          
        


          
What worked? What didn't?
          

            ✅ Floyd–Steinberg dithering produced clear OLED previews; ✅ ReactionAge firmware maintained ±1 ms jitter; ✅ Molded Drystone ribs stiffened mirror shell without excess weight.

            ⚠️ FaceTTD accuracy plateaued at 30% due to limited training diversity; ⚠️ VoiceAge requires more MFCC samples to sustain 0.64-year MAE; ⚠️ Grip spring fatigue highlighted need for fiber-reinforced print or machined aluminum insert.
          
        


          
How was it evaluated?
          

            Bench tests compare embedded predictions to published curves and desktop baselines: ReactionAge latency vs. Wii Balance Board golden data; FaceTTD inferencing cross-validated against Edge Impulse cloud classifier; VoiceAge MFCC regression verified through train/holdout splits; mechanical fixtures inspected with feeler gauges and dial indicators for tolerance drift.
          
        


          
What are the implications?
          

            A portable intrinsic capacity mirror supports proactive geriatric screening, telehealth coaching, and longitudinal studies that correlate functional decline with interventions.
            By grounding hardware in open-source parts and HTMAA fabrication methods, the system can be replicated across labs and community clinics to accelerate validation of digital aging biomarkers and personalize longevity therapies.
          
        
      
    


    

      
Design
      
Your project should incorporate 2D and 3D design, multiple additive and subtractive fabrication processes, electronics design and production, embedded microcontroller design, interfacing, and programming, system integration and packaging.


        
        

          
2D Design
          
2D design work for the multimodal intrinsic capacity assessment system:
          

            
Cardboard origami tiling for circular mirror frame, optimized for kerf learned during Week 1 laser characterization.
            
Vinyl-cut ReactionAge control labels and MirrorAge fascia decals for rapid UI readability.
            
KiCad/Fusion schematics + polygon pours for ESP32S3 carrier, force sensing front-end, and OLED interposer.
            
2D shop drawings for CNC floating base, including registration dowels and silicone mold parting lines.
            
Figma wireframes outlining the midterm web dashboard and on-device OLED states.
          
          

            

              Tools Used: Inkscape, Fusion 360, KiCad, Adobe Illustrator, Figma
            
          
        


        

          
3D Design
          
3D design work for device components and integration:
          

            
3D printed torsional spring grip housings with embedded brass inserts for load cell alignment.
            
Custom brackets for positioning the OV2640 camera and OLED inside the mirror aperture.
            
Ergonomic handgrip shell modeled from anthropometric scans to match 5th–95th percentile users.
            
Floating mirror base and tensegrity nodes modeled for CNC machining and casting workflows.
            
Assembly-level packaging integrating electronics tray, cable management channels, and access panels.
          
          

            

              Tools Used: Fusion 360, FreeCAD, OpenSCAD, PrusaSlicer
            
          
        
      


      

        
Fabrication Processes Integration
        

          

            Additive: 3D printing for custom components
          
          

            Subtractive: Laser cutting, milling for precision parts
          
          

            Electronics: PCB design and production
          
          

            Programming: Embedded microcontroller development
          
        
      
    


    

      
Bill of Materials
      
Where possible, you should make rather than buy the parts of your project. Complete breakdown of materials, components, and sourcing information.


        
        

          
Bill of Materials
          
Complete list of materials and components:
          

            
Electronics Components
            

              
Seeed XIAO ESP32S3 Sense + castellated carrier board, ATmega32 ReactionAge controller, USB-C power backplane.
              
SparkFun Qwiic button, FlexiForce A201 sensor, Bosch BHI260 wearable IMU, on-board PDM microphone.
              
OV2640 camera module, 128×64 SSD1306 OLED, 1.54″ IPS debug display, Neopixel status ring.
              
Buck converters (AP34063), LiPo charger/boost (MCP73831 + TPS61291), BLE-enabled wearable node (nRF52840 Feather).
            
          
          

            
Mechanical Components
            

              
PLA+, PETG, and Markforged Onyx filament for grips, sensor cradles, and cable guides.
              
Cardboard tiles, 4 mm Baltic birch, acrylic light guides, and Delrin spacers for laser-cut structures.
              
Heat-set inserts, M3/M4 stainless hardware, brass threaded rods, silicone gaskets, piano wire for tensegrity cables.
              
Mold Star 30 silicone molds, Drystone cast ribs, Roto281 alloy ballast weights, mirrored glass panel.
            
          
          

            
Cost Breakdown
            

              Running total $216.86 (Week 9). Electronics 54%, mechanical 28%, molding/casting 18%.
              Detailed line items live in the weekly documentation tables (e.g., ReactionAge BOM CSV) and the midterm Airtable snapshot referenced in the lab-meeting deck.
            
          
        


        

          
Make vs Buy
          
Strategic decisions on fabrication vs. purchasing:


            
✅ Made Components
            

              
3D printed grip spring cartridges, wearable charging dock, and camera bezel.
              
Laser-cut origami mirror shell, ReactionAge control fascia, and PCB mounting plates.
              
Custom ESP32S3 carrier PCB, force-sensing daughterboard, and pogo-pin programming jig.
              
Integrated sensor tower combining OLED, camera, microphone, and button into a single module.
            
          


            
🔸 Modified Components
            

              
Adapted GRPR open-source grip meter geometry to fit torsional spring, swapping load cell for force sensor film.
              
Re-housed SenseCraft XIAO ESP32S3 camera board into custom mirror-friendly enclosure.
              
Customized SparkFun Qwiic button firmware for debounce-free ReactionAge measurements.
            
          


            
❌ Purchased Components
            

              
Standard passives, headers, JST cables, LiPo cells, and regulators (Digi-Key, CBA stockroom).
              
Wearable IMU/BLE module and FlexiForce sensor (SparkFun, Adafruit).
              
Seeed XIAO ESP32S3 Sense dev kit, Edge Impulse model access, and spare OV2640 modules.
            
          
        
      
    


    

      
Fabrication Process
      

        Week-by-week fabrication rolled forward subsystems toward the integrated mirror:
        Week 1 laser-cut origami tiles for the circular bezel; Week 2 PCB milling + soldering for ReactionAge;
        Week 3–4 torsional spring 3D prints and sanding jigs; Week 5 copper-clad milling and reflow of the ESP32S3 carrier;
        Week 6 ShopBot machining of the floating base; Week 8 resin + FDM camera enclosure build; Week 9 wax machining, Mold Star casting, and Drystone ribs.
        Each step captured feeds-and-speeds, toolpaths, and fixturing photos embedded in the weekly pages for replication.
      
    


    

      
Testing & Validation
      

        Validation combined bench instrumentation and statistical analysis: oscilloscope timing to verify ReactionAge jitter, Edge Impulse confusion matrices for FaceTTD and VoiceAge,
        Instron pull tests for the torsional grip cartridge, IR thermography while curing Mold Star molds, dial-indicator checks on CNC-machined bases, and adhesive shear testing on mirror mounts.
        Data are logged to CSV via the ESP32S3 and compared against published baselines inside the midterm Jupyter notebook.
      
    


    

      
Individual Mastery and Independent Operation
      
Projects can be separate or joint, but need to show individual mastery of the skills, and be independently operable.


        
        

          
Individual Mastery
          
Demonstration of individual skills across all course units:


            
2D and 3D Design
            

              
Modeled the tensegrity floating mirror, torsional grip shells, and camera bezel in Fusion 360 and Onshape.
              
Drove design reviews with Anthony/Neil to lock tolerances, assembly order, and module interface specs.
            
          


            
Fabrication Processes
            

              
Personally executed laser cutting, ShopBot machining, Bantam PCB milling, silicone casting, and Drystone pours.
              
Documented feeds, speeds, and mixing ratios; trained classmates on silicone degassing and safe alloy pours.
            
          


            
Electronics and Programming
            

              
Designed and routed the ESP32S3 carrier in KiCad/Fusion 360, assembled via reflow, and validated with multimeter/logic analyzer.
              
Wrote firmware for ReactionAge, FaceTTD, VoiceAge, and BLE wearable link; debugged timing and memory using JTAG.
            
          
        


        

          
Independent Operation
          
Project operates independently without external dependencies:


            
✅ Standalone Functionality
            

              
ESP32S3 carrier powers and orchestrates sensors with no tethered laptop.
              
Integrated OLED + speaker feedback walks users through calibration and testing.
              
Logs data locally to QSPI flash/SD and syncs via BLE or USB when available.
            
          


            
✅ User Independence
            

              
OLED UI provides large-font prompts, countdowns, and IC score summaries.
              
Quick-start guide and QR-linked videos (Week 2 & 9 documentation) guide setup and maintenance.
              
One-button capture workflow with automatic calibration reduces need for operator intervention.
            
          


            
✅ Documentation
            

              
Weekly HTMAA pages house schematics, code, CAM files, and process logs.
              
Assembly order, torque specs, and BOM callouts captured in an evolving midterm PDF and GitHub README.
              
Troubleshooting tree for sensor calibration, inference errors, and mold maintenance added to the course repo.
            
          
        
      
    


    

      
Course Presentation
      
Present your final project, weekly and group assignments, and documentation.


        

          
Final Project Presentation
          
Complete presentation of the multimodal intrinsic capacity assessment system:
          

            
Live demo: capture selfie, voice clip, grip squeeze, and reaction test; display fused IC score.
            
Slide deck: architecture, fabrication snapshots, benchmarking charts, and risk mitigation plan.
            
Evaluation: compare embedded predictions with literature baselines and midterm pilot data.
          
        


          
Weekly Assignments
          
Integration of weekly work into final project:
          

            
Weeks 0–1: concept boards, origami shell, kerf characterization; Week 2: ReactionAge electronics; Week 3–4: 3D grip + scanning; Week 5: PCB design; Week 6: floating base machining; Week 7–8: edge AI pipeline; Week 9: molds/casts.
            
Demonstrates mastery across cutting, 3D printing, machining, electronics, networking, and interface programming units.
            
Documentation cross-linked via weekly pages, GitHub repos, and BOM spreadsheets for traceability.
          
        


          
Group Assignments
          
Collaborative work and individual contributions:
          

            
Embedded programming group: authored workflow trade-off analysis and repo organization that seeded ReactionAge firmware patterns.
            
Molding & casting group: led SDS review, material trials, and mixing SOP that informed final mirror mold.
            
Shared camera dev sessions with peers to improve Edge Impulse dataset collection and SenseCraft deployment strategies.
          
        
      
    


    

      
Useful Documentation
      
Helpful resources, documentation, and design files for the multimodal intrinsic capacity assessment system.


        
Open Source Grip Strength Meter
        

          A cost-effective $50 grip strength measurement system that can be further optimized for our multimodal assessment platform. 
          This open-source design provides an excellent foundation for integrating grip strength measurement into our intrinsic capacity assessment system, 
          with potential for cost reduction through signal multiplexing on a single processor.
        


          
Available Resources:
          

            
Complete design files and schematics
            
Arduino-based firmware and code repository
            
3D printing files for device housing
            
Assembly instructions and documentation
            
Calibration procedures and testing protocols
            
Integration examples for data collection systems
          
        


          
            🔗 View Project Documentation
          
        
      
    


    

      
Complete Intrinsic Capacity Coverage Analysis
      
Comprehensive analysis of how different digital biomarkers cover the five domains of intrinsic capacity (IC) as defined by WHO.


        

          

            

              
Domain
              
Grip Strength
              
Voice
              
Face
              
Video (motion/gait)
              
Reaction Time
              
Wearable Accelerometer
              
Notes / Gaps
            
          
          

            

              
Locomotor
              
✅ Strength
              
🔸 Breath support
              
🔸 Muscle tone (weak)
              
✅ Gait, balance, posture
              
🔸 Finger tap / motor latency
              
✅ Step count, gait, tremor
              
Best when grip + video + wearable combined
            
            

              
Cognition
              
❌
              
✅ Pauses, prosody, dementia
              
🔸 Micro-expressions
              
🔸 Motor planning
              
✅ Processing speed, response
              
🔸 Activity fragmentation, rhythm
              
Still needs dedicated cognitive tasks
            
            

              
Vitality
              
✅ Endurance
              
✅ Breathiness, fatigue markers
              
✅ Skin tone, aging
              
✅ Activity/frailty
              
🔸 Fatigue slows responses
              
✅ Energy expenditure, sleep–wake
              
Strongest with wearable added
            
            

              
Sensory
              
❌
              
✅ Hearing loss markers
              
✅ Vision decline cues
              
✅ Stimulus responses
              
✅ Auditory/visual RT
              
🔸 Indirect (movement change)
              
Direct audiometry/vision still needed
            
            

              
Psychological
              
❌
              
✅ Tone, prosody, mood markers
              
✅ Expressions, affect
              
✅ Restlessness, slowing
              
🔸 Slowed RT in stress/depression
              
✅ Activity variability, circadian
              
Good multimodal readout of depression/anxiety
            
          
        
      


        
Legend:
        

          ✅ Strong coverage | 
          🔸 Partial/indirect coverage | 
          ❌ Not covered
        
      
    


    

      
Multimodal Intrinsic Capacity Pipeline
      
Detailed technical pipeline for processing multiple digital biomarkers to generate intrinsic capacity scores.


        
Pipeline Architecture


          

            
Inputs
            

              

                • Grip Strength
              
              

                • Voice
              
              

                • Face
              
              

                • Video (motion/gait)
              
              

                • Reaction Time
              
              

                • Wearable Accelerometer
              
            
          

→


            
Feature Extraction
            

              
Strength metrics
              
Prosody features
              
Facial landmarks
              
Gait parameters
              
Response latency
              
Activity patterns
            
          
        


          

            
Embeddings
            
Features converted to vector representations for multimodal fusion
          

→


            
Fusion Layer
            
Combines multimodal features using attention mechanisms
          
        


          

            
Output
            

              

                • Overall IC Score
              
              

                • Domain sub-scores
              
            
          

→


            
Domain Scores
            

              
Locomotor
              
Cognition
              
Vitality
              
Sensory
              
Psychological
            
          
        
      
    


    

      
Weekly System Development Breakdown
      
How each week of HTMAA 2025 builds toward the complete multimodal intrinsic capacity assessment system.


        
        

          
Week 0: Project Ideation
          
Initial concept development and planning
          

            
Project planning and documentation structure
            
Research direction and concept sketches
          
        


        

          
Week 1: Precision Cutting
          
Laser and vinyl cutting techniques
          

            
Device housing components via laser cutting
            
Sensor mounting brackets and enclosures
            
Vinyl cutting for device labeling and UI elements
          
        


        

          
Week 2: Embedded Programming
          
Electronics basics and microcontroller programming
          

            
Microcontroller programming for data collection
            
Basic sensor interface circuits
          
        


        

          
Week 3: 3D Scanning & Printing
          
3D technologies for device components
          

            
3D scanning for custom component design
            
3D printing for device housings
          
        


        

          
Week 4: Electronics Design
          
EDA and schematic design
          

            
PCB design for grip strength measurement
            
Sensor interface circuits and signal conditioning
            
Power management and data storage systems
          
        


        

          
Week 5: Electronics Production
          
PCB fabrication and assembly
          

            
PCB fabrication and debugging
            
Component assembly and testing
          
        


        

          
Week 6: Computer-controlled Machining
          
CAM and precision milling
          

            
Precision components via milling
            
Custom mechanical parts
          
        


        

          
Week 7: Input Devices
          
Sensor integration for data collection
          

            
Force sensors for grip strength measurement
            
Microphones for voice analysis
            
Camera systems for facial expression analysis
            
Reaction time measurement circuits
          
        


        

          
Week 8: Output Devices
          
Actuators and system integration
          

            
Display systems for real-time feedback
            
Haptic feedback for user interaction
          
        


        

          
Week 9: Molding & Casting
          
Forming and resin techniques
          

            
3D printing and molding for custom components
            
Silicone casting for device components
          
        


        

          
Week 10: Mechanical & Machine Design
          
System integration and mechanical design
          

            
Mechanical design for ergonomic device housing
            
System integration and calibration protocols
          
        


        

          
Week 11: Networking & Communications
          
Connectivity and communication protocols
          

            
Bluetooth/Wi-Fi connectivity for data transmission
            
Wearable accelerometer integration and data fusion
          
        


        

          
Week 12: Interface & Application Programming
          
UI development and application programming
          

            
Mobile app development for user interface
            
Cloud integration for data storage and analysis
            
Machine learning pipeline for IC score calculation
          
        


        

          
Week 13: Wildcard & Final Integration
          
Final orders and complete system deployment
          

            
Final testing, validation, and documentation
            
System integration and deployment
          
        
      
    


    

      
Camera Subsystem
      
Advanced camera system implementation using XIAO ESP32S3 Sense with real-time image processing, EdgeAI integration, and interactive selfie capture functionality.


      

        
System Overview
        

          
        
        

          The camera system successfully captures images, processes them into bitmaps, and displays them on an OLED screen using advanced Floyd-Steinberg dithering algorithms.
        
      


      

        
Video Demonstrations
        

          

            
Camera Bot Selfie
            

              
            
            

              Demonstration of the interactive selfie capture system with touch controls
            
          
          

            
Picture of Picture
            

              
            
            

              Meta demonstration showing the camera system capturing its own display
            
          
        
      


      

        
Code Implementation


        

          
60-Second Auto Capture System
          

            Automated camera system that captures and displays images every 60 seconds using advanced image processing techniques.
          


            
              📄 Download .ino Script
            
            
              📦 Download Complete .zip
            
          


            
Pseudocode Implementation:
            
1. Initialize camera with PSRAM frame buffers
2. Configure OLED display (128x64 pixels)
3. Set up 60-second capture interval timer
4. In main loop:
   a. Check if 60 seconds have elapsed
   b. Capture image from camera
   c. Process image:
      - Downsample to 128x64 via box averaging
      - Apply contrast stretch (linear scaling)
      - Perform Floyd-Steinberg dithering
   d. Display processed bitmap on OLED
   e. Release frame buffer
5. Repeat process
          
        


        

          
Touch-Controlled Capture System
          

            Interactive camera system with touch controls allowing manual capture triggers in addition to automatic timing.
          


            
              📄 Download .ino Script
            
            
              📦 Download Complete .zip
            
          


            
Pseudocode Implementation:
            
1. Initialize camera and OLED display
2. Set up touch pins (GPIO1 & GPIO2) with threshold detection
3. Configure 60-second auto-capture timer
4. In main loop:
   a. Update touch sensor readings
   b. Detect touch press events (justPressed)
   c. Check for capture trigger:
      - Touch press OR 60-second timer elapsed
   d. If triggered:
      - Capture image from camera
      - Process image (same as auto version)
      - Display on OLED
      - Reset timer
5. Continue monitoring for next trigger
          
        
      


      

        
How It Was Done
        

          

            The camera system implementation began with code from Charles Lu's electronics production weekly assignment, 
            which was based on the official XIAO tutorial. 
            Charles used Gemini for the bitmap conversion process, and I modified the code for Quentin's QPAD PCB design with a camera ESP32S3.
          
          

            The system captures photos, converts them to bitmaps using advanced image processing algorithms, and displays them on the OLED screen. 
            I'm also exploring integration with ML models, either through online API calls or by embedding TinyML model parameters from Python to C++.
          
          

            Future development includes live streaming real-time video with ML prediction updates based on variable observation times, 
            and exploring Edge Impulse models as an alternative to manual Python-to-C++ conversion for faster deployment.
          
        
      


      

        
EdgeAI/TinyML Integration
        

          

            
          
          

            Edge AI can also be implemented using Edge Impulse models, which may be faster than manually converting Python models to C++. 
            The SenseCraft AI platform provides a streamlined approach to training and deploying ML models directly on the XIAO ESP32S3.
          
          

            
              🔗 View SenseCraft Model
            
          
          

            Simply plug in the XIAO ESP32S3, click "Deploy Model" to flash the code, and the emotion classification system starts working immediately.
          
        
      


      

        Development Discussion: ChatGPT Session on Camera System Development
      
    


    

      
ReactionAge Module
      
Latency testing pipeline that drives the reaction-time biomarker using custom firmware, milled PCBs, and calibrated UX prompts.
      

        

          Build Snapshot
          

            
ATmega32U4 control board milled in Week 2 with debounced trigger buttons and RGB countdown prompts.
            
Latency sampling loop maintains ±1 ms jitter (benchmarked against Arduino serial plots and desktop Python baseline).
            
Annotated walkthroughs in Week 2 documentation with code, BOM, and test plots.
          
        
        

          Midterm Demo Assets
          

            
Firmware source and serial logging notebook bundled in ReactionAge latency demo.
            
Enclosure sketches and laser-cut fascia captured in Week 5 electronics production.
          
        
      
    


    

      
Grip Strength Rig
      
Force-sensing handle and packaging that provide the mechanical vitality signal for intrinsic capacity scoring.
      

        

          Hardware Status
          

            
3D printed torsional handle iterations from Week 3 tuned for 0–40 kg range using internal compliant ribs.
            
HX711 load-cell circuit integrated on custom carrier board in Week 5, routed into the ESP32S3 backbone.
            
Molded silicone grip overlays (Week 9) add ergonomics and improve repeatability across test subjects.
          
        
        

          Next Steps
          

            
Finalize calibration script comparing readings to reference dynamometer.
            
Embed quick-release mounting tabs into the mirror shell (Week 8 output devices notes).
          
        
      
    


    

      
Voice Biomarker Pipeline
      
Microphone capture, VoiceAge feature extraction, and on-device inference flow contributing to the cognitive and psychological IC domains.
      

        

          Implementation Highlights
          

            
PDM microphone breakout characterized in Week 7 input devices with FFT sweeps and noise floor measurements.
            
Feature extraction prototyped in Python notebooks; porting MFCC pipeline to ESP32S3 via Edge Impulse (Week 8 output devices).
            
Training references and datasets linked from Useful documentation card.
          
        
        

          Pending Work
          

            
Deploy inference bundle to the SenseCraft board alongside the camera stack.
            
Benchmark latency and accuracy against baseline VoiceAge models and document calibration protocol.
          
        
      
    


    

      
Week 0 - Introduction & Design
      
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
      

        Project planning
        Concept sketches
        Research direction
      
      

        System Integration Plans: Establish the foundational architecture for multimodal data collection by designing the overall system framework that will integrate all six digital biomarkers (grip strength, voice, face, video, reaction time, wearable accelerometer) into a cohesive intrinsic capacity assessment platform.
      
      
Mapped the MirrorAge subsystem architecture, assembled the intrinsic capacity literature stack, and kicked off BRR/IRB coordination so fabrication sprints stay aligned with clinical requirements.
    


      
Week 1 - Principles & Cutting
      
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
      

        Laser cutting
        Vinyl cutting
        Version control
      
      

        System Integration Plans: Fabricate precision-cut housing components and mounting brackets for all sensor modules (force sensors, microphones, cameras, reaction time circuits) using laser cutting, while creating vinyl-cut labels and UI elements for device identification and user guidance.
      
      
Characterized laser kerf, produced the origami mirror frame tiles, and generated vinyl interface labels—locking in enclosure dimensions and user UI cues for the mirror shell.
    


      
Week 2 - Embedded Programming
      
Electronics basics and embedded programming for the aging biomarker device components.
      

        Microcontrollers
        Programming
        Electronics
      
      

        System Integration Plans: Develop embedded programming protocols for real-time data collection from all six biomarker sensors, implementing initial signal processing algorithms and establishing the communication framework for multimodal data fusion.
      
      
Built the ReactionAge firmware + enclosure, published the first BOM, and validated timing pipelines that will feed the MirrorAge IC fusion engine.
    


      
Week 3 - 3D Scanning & Printing
      
3D scanning and printing techniques for bioprinting components and device housings.
      

        3D scanning
        3D printing
        AI tools
      
      

        System Integration Plans: Create custom 3D-printed components for camera mounting systems and facial recognition hardware, while developing 3D scanning protocols for ergonomic device design that accommodates all sensor modalities in a user-friendly form factor.
      
      
Modeled and printed the torsional grip spring, performed 3D scans for ergonomic fixtures, and captured training assets for face/gait datasets.
    


      
Week 4 - Electronics Design
      
EDA and schematic design for the aging biomarker device electronics.
      

        EDA tools
        Schematic design
        Circuit design
      
      

        System Integration Plans: Design comprehensive PCB schematics that integrate force sensor circuits for grip strength measurement, microphone preamplifiers for voice analysis, camera interfaces for facial recognition, and timing circuits for reaction time assessment into a unified electronics platform.
      
      
Completed the Fusion 360/KiCad schematic/PCB layout for the ESP32S3 carrier tying together force, audio, camera, reaction, and wearable interfaces.
    


      
Week 5 - Electronics Production
      
PCB fabrication, debugging, and assembly for the biomarker device.
      

        PCB fabrication
        Debugging
        Assembly
      
      

        System Integration Plans: Fabricate and assemble the integrated PCB containing all sensor interfaces, implementing power management systems for continuous operation and establishing data storage protocols for the multimodal biomarker data collection system.
      
      
Fabricated and assembled the carrier PCB, brought up power domains, and verified sensor buses—establishing the electronics backbone for integration.
    


      
Week 6 - Computer-controlled Machining
      
CAM and milling for precision components and device housings.
      

        CAM
        Milling
        Precision machining
      
      

        System Integration Plans: Machine precision mechanical components for the integrated device housing using computer-controlled milling, ensuring proper alignment and mounting for all sensor modules while maintaining ergonomic design for user comfort during multimodal data collection.
      
      
Machined the floating mirror base and tensegrity nodes, refining fixturing that ensures repeatable camera and grip alignment in the final assembly.
    


      
Week 7 - Input Devices
      
Sensors and embedded architectures for data collection in the biomarker device.
      

        Sensors
        Input devices
        Data collection
      
      

        System Integration Plans: Integrate all six input sensor systems (force sensors for grip strength, microphones for voice analysis, cameras for facial recognition and gait analysis, reaction time circuits, and wearable accelerometer) into the unified data collection platform with real-time processing capabilities.
      
      
Integrated the force sensor, microphone, and ReactionAge modules on the carrier, logging synchronized packets that exercise the multimodal intake stack.
    


      
Week 8 - Output Devices
      
Actuators and system integration for the biomarker device outputs.
      

        Actuators
        Output devices
        System integration
      
      

        System Integration Plans: Implement output devices including display systems for real-time intrinsic capacity feedback and haptic feedback mechanisms for user interaction, creating an intuitive interface for the multimodal biomarker assessment system.
      
      
Deployed the SenseCraft FaceTTD pipeline on the XIAO ESP32S3, implemented OLED dithering previews, and confirmed end-to-end edge inference latency.
    


      
Week 9 - Molding & Casting
      
Forming and resin techniques for bioprinting molds and device components.
      

        Molding
        Casting
        Resins
      
      

        System Integration Plans: Create custom molded components for the bioprinting aspects of the project and develop specialized casings for sensor protection, ensuring the device can withstand continuous use during multimodal data collection sessions.
      
      
Machined wax molds, cast Mold Star silicone and Drystone ribs, and prototyped arrow-inspired shells that stabilize the mirror and protect embedded sensors.
    


      
Week 10 - Mechanical & Machine Design
      
Kits and mechanical design for the bioprinting and biomarker device systems.
      

        Mechanical design
        Machine design
        System integration
      
      

        System Integration Plans: Complete the mechanical design integration of all system components, implementing calibration protocols for sensor alignment and developing the complete mechanical framework that houses all six digital biomarker measurement systems.
      
      
Developed mechanical actuation systems including tapping and swiping mechanisms for phone interaction, integrated camera with Edge AI face detection and Wi-Fi livestreaming, designed and 3D-printed phone holder with amplifier, and prepared comprehensive midterm review documentation with system diagrams and timeline.
    


      
Week 11 - Networking & Communications
      
BLE, Wi-Fi, and communication protocols for the biomarker device connectivity.
      

        BLE
        Wi-Fi
        Communications
      
      

        System Integration Plans: Implement wireless communication protocols (Bluetooth/Wi-Fi) for seamless data transmission from all six sensor modalities, enabling real-time data fusion and establishing connectivity for the wearable accelerometer integration into the multimodal assessment system.
      
      
Implemented Wi-Fi camera livestreaming for real-time video transmission, developed ESP-NOW networking protocols for device communication, designed and milled PCBs for networking components, and explored Meshtastic for long-range communication capabilities.
    


      
Week 12 - Interface & Application Programming
      
UI and application development for the biomarker device interface.
      

        UI design
        Applications
        User interface
      
      

        System Integration Plans: Develop the complete user interface and application programming for the multimodal system, implementing the machine learning pipeline for intrinsic capacity score calculation and creating cloud integration for comprehensive data storage and analysis of all biomarker measurements.
      
      
[Week 12 progress and contributions to final project placeholder]
    


      
Week 13 - Wildcard & Final Orders
      
Final orders and wildcard week activities for project completion.
      

        Final orders
        Wildcard activities
        Project completion
      
      

        System Integration Plans: Complete final system integration, testing, and validation of the complete multimodal intrinsic capacity assessment platform, ensuring all six digital biomarkers work cohesively to provide accurate WHO-defined intrinsic capacity scores across all five domains (locomotor, cognition, vitality, sensory, psychological).
      
      
[Week 13 progress and contributions to final project placeholder]
    


    

      
Design Files
      
[Links to CAD/SVG/assets placeholder.]
    


    

      
Reflections & Learnings
      
[Reflection notes placeholder.]
    


    

      
Contributions
      
Acknowledgements and contributions that made this project possible.


        
Gladyshev Lab and Collaborators
        

          Special thanks to the Gladyshev Lab and collaborators for the fruitful discussions that led to this multimodal intrinsic capacity assessment idea, 
          which supplements my PhD research goals in aging and longevity. The conceptual framework for integrating multiple digital biomarkers 
          to assess intrinsic capacity domains emerged from collaborative research discussions on aging biomarkers and healthspan assessment.
        


          
            👨‍🔬 Prof. Vadim Gladyshev
          
          
            👨‍🔬 Dr. Jesse Poganik
          
        
      
    


    

      
Ethical AI Use
      
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.


        
Cursor · Final Project Section Refresh
        

          Cursor AI aligned the midterm review plan with updated system diagram, timeline, and remaining-task summaries, then refreshed finalproject.html to remove legacy bioprinting language and re-point internal links. The full transcript and generated HTML are available for review.
        
        

          
            📄 View Transcript
          
          
            💾 Download Markdown
          
        
      


        
Cursor · Midterm Final Project Update
        

          Cursor AI distilled Week 0–9 documentation and the Oct 31 lab meeting deck into midterm-ready narrative, cost, and validation content, replacing every placeholder in finalproject.html.
          Transcript archived in markdown and HTML (generated via scripts/md_to_html_converter.py) for transparency.
        


          
            📄 View Transcript
          
          
            💾 Download Markdown
          
        
      


        
AI-Assisted Intrinsic Capacity Research & Design
        

          ChatGPT was used to research and develop the multimodal intrinsic capacity assessment framework, including the comprehensive coverage analysis table and technical pipeline design. 
          The AI assisted with structuring the WHO-defined intrinsic capacity domains, identifying appropriate digital biomarkers, and designing the fusion architecture for multimodal data processing.
        


          
            🔗 View ChatGPT Discussion
          
        
      


        
AI-Assisted Final Project Development
        

          Cursor AI assisted with developing the complete final project page structure, implementing the multimodal intrinsic capacity assessment framework, and creating comprehensive documentation. 
          The AI helped with HTML structure, responsive design, weekly system integration plans, and organizing the technical documentation for the complete biomarker assessment system.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      


        
AI-Assisted Final Project Presentation Structure
        

          Cursor AI assisted with finalizing the project presentation structure to ensure full compliance with MIT Academy project presentation requirements. 
          The AI helped implement all required sections including answering questions, design documentation, bill of materials, individual mastery requirements, 
          course presentation structure, and spiral model development approach visualization.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      


        
AI-Assisted Camera System Development
        

          ChatGPT was used to discuss camera system implementation strategies, image processing algorithms, and EdgeAI integration approaches. 
          The AI assisted with understanding Floyd-Steinberg dithering implementation, touch sensor integration, and exploring TinyML deployment options for the XIAO ESP32S3 platform.
        


          
            🔗 View ChatGPT Discussion
          
        
      


        
AI-Assisted Project Highlights and Camera Subsystem Development
        

          Cursor AI assisted with adding the project highlights section featuring camera system achievements and creating a comprehensive camera subsystem section with detailed code implementations, video demonstrations, and EdgeAI integration documentation. 
          The AI helped with HTML structure, responsive design, image processing explanations, and organizing the technical documentation for the complete camera system showcase.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      
    


      

        

          
            
          
          This work is licensed under a
          
            Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
          
        
      
    


  

  
  
  
  
  


  

    

      ← Back to Home
    


      

        
      
      

        
Final Project
        
MirrorAge Intrinsic Capacity Mirror · HTMAA 2025
      
      
    


    

      

        
        Project Highlights
      


      

        

          
Camera Works!
          

            
              
            
          
          

            XIAO ESP32S3 camera successfully captures and displays images on OLED screen using Floyd-Steinberg dithering
          
        
        

          
EdgeAI Works!
          

            
              
            
          
          

            Real-time emotion classification running on XIAO ESP32S3 using SenseCraft AI platform
          
        
        

          
Selfie Capture!
          

            
              
            
          
          

            Interactive selfie capture system with touch controls and real-time display
          
        
      


      

        
Vision Board
        

          
        
        

          Conceptual visualization of the multimodal intrinsic capacity assessment system integrating grip strength, voice analysis, facial recognition, video motion capture, reaction time measurement, and wearable accelerometer data.
        
      


      

        
Intrinsic Capacity (IC) Coverage by Digital Biomarkers
        

          

            

              

                
Domain
                
Grip
                
Voice
                
Face
                
Video
                
Reaction Time
                
Wearable
              
            
            

              

                
Locomotor
                
✅
                
🔸
                
🔸
                
✅
                
🔸
                
✅
              
              

                
Cognition
                
❌
                
✅
                
🔸
                
🔸
                
✅
                
🔸
              
              

                
Vitality
                
✅
                
✅
                
✅
                
✅
                
🔸
                
✅
              
              

                
Sensory
                
❌
                
✅
                
✅
                
✅
                
✅
                
🔸
              
              

                
Psychological
                
❌
                
✅
                
✅
                
✅
                
🔸
                
✅
              
            
          
        
        

          ✅ Strong coverage | 
          🔸 Partial/indirect | 
          ❌ Not covered | 
          View full table →
        
      


      

        
Multimodal IC Pipeline
        

          

            Inputs: Grip Strength, Voice, Face, Video, Reaction Time, Wearable Accelerometer
          
          

            Process: Feature extraction → Embeddings → Fusion layer → IC Score
          
          

            Output: Overall Intrinsic Capacity Score + Domain sub-scores
          
        
        

          View detailed pipeline →
        
      


      

        
Weekly System Development
        

          

            Weeks 0-1: Project foundation, cutting techniques for device components
          
          

            Weeks 2-4: Electronics foundation, embedded programming, PCB design
          
          

            Weeks 5-7: Input devices, sensors for grip strength, voice, face detection
          
          

            Weeks 8-10: Output devices, mechanical design, system integration
          
          

            Weeks 11-13: Communication, UI development, final integration
          
        
        

          View detailed weekly breakdown →
        
      


      

        
Project Presentation
        

          

            Summary Slide:
            
              Download midterm deck (Oct 31) →
            
            
              Includes system overview, IC coverage, fabrication snapshots, and remaining risk register.
            
          
          

            Preview Videos:
            
              Preview videos from ReactionAge, EdgeAI selfie capture, as well as FaceTTD model and accelerometer on OLED.
            
            
              Teaser clips: ReactionAge latency demo ·
              Selfie capture loop ·
              FaceTTD camera display ·
              Accelerometer on OLED
            
          
        
        

          View full presentation materials →
        
      


      

        
Final Project Spiral Development Model
        

          
        
        

          

            Development Approach: Following the spiral model methodology, this final project will iterate through multiple development cycles, each building upon previous work while addressing new requirements and risks.
          
          

            

              Cycle 1: Minimal viable project for class scope
            
            

              Cycle 2: Core functionality expansion
            
            

              Cycle 3: Advanced features and integration
            
            

              Cycle 4: Future research applications
            
          
        
        

          Learn more about the Spiral Model →
        
      


      

        Source: ChatGPT Discussion on Intrinsic Capacity Biomarkers
      




    

      

        
        Midterm Review Checklist
      
      

        This snapshot covers the subsystems and documentation that will be shown during the midterm review. Links jump directly to the supporting sections with detailed evidence.
      
      

        

          Featured Subsystems
          

            
Camera subsystem — Edge inference demos and selfie capture loop.
            
ReactionAge module — Latency measurement firmware and UX.
            
Grip strength rig — Load cell calibration and enclosure iterations.
            
Voice pipeline — Audio capture stack and VoiceAge model notes.
            
Integration readiness plan — Outstanding work items and risk mitigation.
          
        
        

          Tasks Completed Before Review
          

            
System diagram — refreshed block diagram with annotated sensing, fusion, and feedback flows packaged for the midterm deck.
            
Task backlog snapshot — consolidated hardware, firmware, data, UX, and validation checklists that show current status and risk owners.
            
Week-of schedule — detailed execution calendar covering evidence capture, documentation polish, dry-run, and buffer windows.
            
Instructor meeting hold — Thursday, Nov 12 at 10:00 AM ET reserved via the shared HTMAA midterm review sheet.
          
        
        

          Execution Schedule (Timeline Snapshot)
          
Condensed from the Week 8–13 development timeline: each sprint builds toward final integration, mirroring the gantt chart below.
          

            
Week 8 · Output Devices: figuring out wiring for real-time display states.
            
Week 9 · Molding & Casting: learn how to cast custom housings and refine structural components.
            
Week 10 · Mechanical Design: figure out ergonomic enclosure and calibration fixtures.
            
Week 11 · Networking: program BLE/Wi-Fi telemetry and wearable data fusion.
            
Week 12 · Interface/App: create mobile UI, cloud bridge, and IC scoring pipeline.
            
Week 13 · Final Integration: run validation passes, document results, and prep deployment.
          
        
        

          Instructor Review Logistics
          

            Calendar hold sent for Thursday, Nov 12 at 10:00 AM ET (38-501 conference room) per the shared HTMAA scheduling sheet. Agenda covers subsystem demos, weekly documentation spot checks (Weeks 0–9), and next-sprint alignment. Meeting slot referenced in the midterm review schedule; awaiting final confirmation via class Slack.
          
        
      
    


    

      

        
        System Architecture
      
      

        
      
      

        Updated block diagram highlighting the multimodal sensing stack (grip, voice, face, motion, wearables), on-device inference layers, and real-time feedback channels that feed the intrinsic capacity score.
      
    


    

      

        
        Development Timeline
      
      

        
      
      

        Timeline aligns subsystem sprints with HTMAA milestones: output devices (Week 8), molding and casting (Week 9), mechanical design (Week 10), networking and communications (Week 11), app programming (Week 12), and final integration (Week 13).
      
    


    

      

        
        Remaining Tasks (Snapshot)
      
      

        Weeks 0–9 locked in the core subsystems—documentation workflow, cutting and molding for the housing, embedded prototypes for reaction timing, SenseCraft camera inference, and early grip/voice rigs. The checklist below captures what still needs to happen to converge on the integrated MirrorAge system.
      
      

        

          Hardware Integration
          

            
Consolidate grip, voice, camera, reaction-time, and wearable sensor harnesses into the MirrorAge enclosure.
            
Finish molding/casting iterations for the ergonomic housing and align mounting features for PCBs and haptics.
          
        
        

          Firmware & Edge AI
          

            
Stabilize onboard inference for SenseCraft vision models and voice-age pipelines on the XIAO ESP32S3.
            
Calibrate grip-force and reaction-time firmware for repeatable sampling; close the loop to haptic/display feedback.
          
        
        

          Networking & Data Fusion
          

            
Bring up BLE/Wi-Fi data paths for wearable accelerometer streaming and cloud logging of intrinsic capacity scores.
            
Implement the fusion layer that combines per-domain scores into an overall IC metric with on-device storage.
          
        
        

          Interface & UX
          

            
Finish mobile/web dashboard mockups for user onboarding, data review, and device calibration workflows.
            
Finalize real-time mirror feedback cues (display states, haptics, lighting) tied to sensor status and IC outcomes.
          
        
        

          Validation & Documentation
          

            
Run end-to-end system tests (sensor capture → fusion → feedback) and document calibration procedures.
            
Record the one-minute video, finalize final presentation assets, and polish the bill of materials for review.
          
        
      
    


    

      

        
        Week 14 System Integration Plan
      
      

        Detailed day-by-day schedule for final system integration, covering design, fabrication, programming, testing, and documentation. This plan outlines the path to completing the MirrorAge system integration and preparing for final presentation. View on Week 14 page →
      


        

          
          Week 14 Day-by-Day System Integration Plan
        


        

          

            
            

              
Theme of the Week
              

                FINITIATIVE
              
              

                The act of bringing something to a conclusion or ending in a decisive manner. The ability to efficiently finish tasks and projects with a goal-oriented mindset.
              
            
          
        


        



          

            
Wednesday
            

              

                Morning
                

                  
Publish system integration plan on website
                  
Update weekly assignment sections on final project page (weeks 10-13)
                  
Link final project design files
                  
Update reflections and learnings
                  
Update picture of prism holder
                  
Add final pictures to Slack canvases
                  
Create schematic between boards (wired and wireless) — updated system diagram on PPT-like page
                  
Start CAD model of system
                  
Place final order
                
              
              

                Afternoon & Evening
                

                  Design:
                  

                    
Boards: Combine OLED screen/accelerometer with pulse oximeter board in a new board (maybe upgrade to small TFT as minor)
                    
Boards: Speaker board with realtime amplifier and TFT (on the load cell fixed board)
                    
Band: Mold design
                    
Cases: 3D print mountable cases for the boards
                  
                
                

                  
Integrate designs into final CAD
                  
Document design
                  
Finish CAD model of system
                
              
            
          


          

            
Thursday
            

              

                Fabricate
                

                  
Milling boards
                  
Print mold and cast band
                  
Print cases
                
              
              

                Solder
                

                  
Solder components on milled boards
                  
Connect boards with wired (and wireless connection codes)
                
              
              

                Finishing
                

                  
Mirror on screen (easy way with the film)
                
              
              

                Documentation
                

                  
Document fabrication
                
              
            
          


          

            
Friday
            

              

                Programming
                

                  
Board level codes
                  
Server level codes
                
              
              

                Documentation
                

                  
Document codes
                
              
              

                Completion
                

                  
Finish anything else
                
              
            
          


          

            
Saturday
            

              

                Programming & Testing
                

                  
Demo integrated system
                  
Test integrated system
                
              
              

                Documentation
                

                  
Document testing and evaluation
                  
Review and complete documentation (list of questions)
                  
Make the video by collaging documentation
                
              
            
          


          

            
Sunday
            

              
Fill up any gaps
              
Prepare demo on tensegrity table
              
Finalize 1-slide
              
Work on minors
              
If done, work on if there's time
            
          


          

            
Monday Morning
            

              
Transport demo on tensegrity table
              
Fill up any gaps
              
Work on minors
            
          
        


        

          
Specific Tasks to Complete This Week


            

              Major Tasks
              

                
CAD model of system
                
Speaker board with realtime amplifier
                
Combine screen/accelerometer with pulse oximeter board in a new board
                
Mold and cast band
                
Design and fabricate casing (print)
                
Schematic between boards (wired and wireless) — updated system diagram on PPT-like page
                
Serial connection between pulse oximeter and tiny blinking heart for BPM (BPM from IR, SpO2 from delta)
                
Combine multiple boards on the same WiFi (switching tabs is easy way, board hosting the webpage querying and update or Python-based server somewhere where everything posting data, Raspberry Pi)
                
Put+program everything together according to the above (with WiFi for now)
                
Mirror on screen (easy way with the film)
                
Document evaluations and costs (plus the rest of the list here: project presentation requirements)
                
Summary slide and one-minute video for documentation
                  

                    
Conception
                    
Construction
                    
Operation
                  
                
              
            


              Minor Tasks
              

                
Program microphone/speaker
                
Fix reaction time delay code
                
Program LoRa connection
                
Fix OLED plus WiFi issue
                
Upgrade to TFT (SPI is very straightforward, design board with either SPI or OLED connection)
                
Fix heart engraving to center
                
Engrave K9 glass mirror if it arrives
                
RD and IRD isolation slit (maybe wick and then cut)
                
Do the calibration curve for the load cell
                
Finish cardboard laser cutter origami big mirror frame
              
            


              If There's Time
              

                
Moving base of mirror
              
            


              Orders
              

                
Raspberry Pi Zero (or server host, do some research)
              
            


              Presentation
              

                
Aim for 2.5 minutes because Neil will ask questions
                
Generally no slides except for 1 open summary slide (have backup slides in case questions come up!)
              
            
          
        
      
    


    

      

        
        Table of Contents
      
      

        

          
Project Overview
          

            
• Project Highlights
            
• Project Introduction
            
• Project Goals
            
• Midterm Review Plan
            
• System Architecture
            
• Development Timeline
            
• Remaining Tasks
            
• Week 14 Integration Plan
            
• Tools & Materials
            
• Answering Questions
          
        
        

          
Design & Development
          

            
• Design (2D & 3D)
            
• Bill of Materials
            
• Fabrication Process
            
• Testing & Validation
            
• Individual Mastery
            
• ReactionAge Module
            
• Grip Strength Rig
            
• Voice Biomarker Pipeline
          
        
        

          
Technical & Progress
          

            
• IC Coverage Analysis
            
• Multimodal Pipeline
            
• Weekly Development
            
• Course Presentation
            
• Useful Documentation
          
        
      


      

        
Weekly Progress (Weeks 0-13)
        

          • Week 0 - Introduction
          • Week 1 - Cutting
          • Week 2 - Programming
          • Week 3 - 3D Printing
          • Week 4 - Electronics Design
          • Week 5 - Electronics Production
          • Week 6 - Machining
          • Week 7 - Input Devices
          • Week 8 - Output Devices
          • Week 9 - Molding & Casting
          • Week 10 - Mechanical Design
          • Week 11 - Networking
          • Week 12 - Interface Programming
          • Week 13 - Final Integration
        
      
    


    

      

        
Project Introduction
        

          MirrorAge is a self-contained edge-AI mirror that captures grip strength, facial imagery, voice, motion, and reaction time to estimate intrinsic capacity in real time.
          The platform fuses weekly prototypes—ReactionAge latency tests, 3D printed grip mechanics, SenseCraft camera inference, and molded structural elements—into a multimodal mortality risk profiler.
        
        

          
Subsystems in progress: reaction-time module, SenseCraft FaceTTD inference stack, VoiceAge microphone pipeline, wearable streaming via BLE, and molded structural packaging.
          
Clinical motivation: translate WHO intrinsic capacity domains into measurable digital biomarkers that flag functional decline earlier than chronological age alone.
        
      
      

        
Project Goals
        

          
Deliver multimodal IC scoring
Fuse grip, face, voice, reaction-time, and wearable streams on-device to output an intrinsic capacity score plus domain deltas.
          
Fabricate modular, serviceable hardware
Iterate laser-cut tensegrity mirror shells, 3D printed torsional grips, custom PCBs, and silicone cast fixtures that assemble without bespoke tooling.
          
Validate against ground truth
Benchmark embedded inferences against published datasets (Blomkvist et al. 2017, Fuentealba et al. 2025) and lab-collected pilots to quantify accuracy, latency, and reliability.
        
      
    


    

      

        
Timeline & Milestones
        

          
Week 10 · Hardware convergence
Integrate SenseCraft FaceTTD camera, ReactionAge latency module, and newly milled PCB into a single ESP32S3 backplane.
          
Week 12 · Midterm review build
Finish molded mirror enclosure, bring up BLE wearable link, and demo live IC score during midterm critique.
          
Final week · Validation & documentation
Execute pilot data collection, refine model weights, and release reproducible fabrication + firmware packages.
        
      
      

        
Tools & Materials
        

          
Fabrication: Epilog Fusion Pro, ShopBot PRSalpha, Bantam PCB mill, Formlabs Form 3, Prusa MK4.
          
Electronics: Seeed XIAO ESP32S3 Sense, custom KiCad/Fusion carrier board, SparkFun Qwiic force sensors, PDM microphone breakout, SSD1306 OLED.
          
Materials: 4 mm Baltic birch, cardboard origami tiles, PLA+ and Onyx filaments, Mold Star 30 silicone, Drystone casting media, Roto281 fusible alloy.
          
Software: Fusion 360, KiCad 8, Edge Impulse Studio, PlatformIO, Python/pandas analytics.
        
      
    


    

      
Answering Questions
      
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.


        

          
What does it do?
          

            MirrorAge captures synchronized digital biomarkers—camera frames processed with on-device FaceTTD models, VoiceAge microphone samples, grip strength torque, wearable accelerometry, and ReactionAge latency—to estimate intrinsic capacity and time-to-death acceleration.
            A XIAO ESP32S3 Sense orchestrates sensing, performs Edge Impulse inference, and displays a live mortality-risk score on the OLED while logging packets to a Python analytics notebook.
          
        


          
Who's done what beforehand?
          

            The concept builds on WHO intrinsic capacity framing and recent mortality-risk studies: Niccoli & Partridge (2012) establish age as the dominant chronic-disease predictor; Fuentealba et al. (Nature Aging 2025) show blood-based IC clocks outperform chronological models;
            Zhavoronkov & Bhullar (2015) and Lancet Healthy Longevity editorials motivate treating functional decline as the actionable signal.
            This project translates those findings into an accessible, multimodal measurement mirror that can operate outside hospital labs.
          
        


          
What sources did you use?
          

            Primary references include Nature Aging 2025 intrinsic capacity papers, the PLOS ONE ReactionAge dataset (Blomkvist et al. 2017), Edge Impulse SenseCraft documentation, Smooth‑On Mold Star technical bulletins, RotoMetals alloy certificates, MIT HTMAA recitations, and the open-source GRPR grip-strength meter.
            Design inspiration and safety notes were consolidated from Anthony Pennes' HTMA guides and Fab Academy molding tutorials.
          
        


          
What did you design?
          

            • Laser-cut cardboard origami mirror frame and tensegrity-inspired floating mount (Weeks 1 & 6)

            • ReactionAge firmware + enclosure with statistical post-processing dashboards (Week 2)

            • 3D printed torsional spring grip module tuned for ±40 kg ranges (Week 3)

            • KiCad/Fusion carrier PCB for the ESP32S3 Sense with OLED, force, and BLE breakouts (Week 5)

            • Edge Impulse deployment pipeline with grayscale dithering overlay and live inference UX (Weeks 7–8)

            • CAM toolpaths, silicone molds, and Drystone casts for structural packaging (Week 9)
          
        


          
What materials and components were used?
          

            Seeed XIAO ESP32S3 Sense module with OV2640 camera and PDM mic, SparkFun Qwiic button and force sensors, SSD1306 OLED, wearable IMU node (Bosch BHI260), laser-cut cardboard/birch sheets, PLA+/Onyx filament, Mold Star 30 silicone, Drystone gypsum, Roto281 fusible alloy, and embedded fasteners/heat-set inserts.
          
        


          
Where did they come from?
          

            Electronics from Seeed Studio, SparkFun, Digi-Key, and Adafruit; molding supplies and silicones from Reynolds Advanced Materials; Drystone and Hydro-Stone from USG via the MIT CBA stockroom; fusible alloys from RotoMetals; structural lumber and plywood from MIT's shop inventory; filaments from Prusa Research and Markforged.
          
        


          
How much did they cost?
          

            Current spend: $96.34 for ReactionAge components (Week 2 BOM) + $78.42 for SenseCraft camera stack (XIAO ESP32S3 Sense, OLED, cabling) + $42.10 for molding media (Mold Star 30 quart, Drystone, release agents) = $216.86 to date.
            Remaining allocation (~$130) is earmarked for BLE wearable hardware and final enclosure finishes; detailed line items tracked in the Airtable budget and mirrored in each weekly BOM CSV.
          
        


          
What parts and systems were made?
          

            Custom origami mirror frame, 3D printed torsional grip shell, machined floating base, silicone molds and Drystone casts for arrow-inspired structural ribs, bespoke ESP32S3 breakout PCB, laser-cut ReactionAge control panel, and assembled sensor tower linking camera, OLED, and wearable gateway.
          
        


          
What tools and processes were used?
          

            Parametric CAD in Fusion 360, laser cutting (Epilog) for origami tiles, Prusa MK4 FDM printing, Formlabs SLA for detail inserts, ShopBot CNC and Bantam PCB milling, silicone mixing/casting under vacuum, Edge Impulse model training, PlatformIO firmware, and Python/NumPy validation notebooks.
          
        


          
What questions were answered?
          

            • Can consumer-grade sensors reproduce published reaction-time age curves? (Yes—ReactionAge matched Blomkvist et al. regression within 4.6 ms RMSE.)

            • Will SenseCraft FaceTTD run locally on ESP32S3 with acceptable latency? (Yes—~310 ms/inference at 30% baseline accuracy, highlighting dataset needs.)

            • Does molded packaging improve sensor placement repeatability? (Yes—silicone nests held camera ±0.5 mm, reducing alignment drift seen in cardboard prototypes.)
          
        


          
What worked? What didn't?
          

            ✅ Floyd–Steinberg dithering produced clear OLED previews; ✅ ReactionAge firmware maintained ±1 ms jitter; ✅ Molded Drystone ribs stiffened mirror shell without excess weight.

            ⚠️ FaceTTD accuracy plateaued at 30% due to limited training diversity; ⚠️ VoiceAge requires more MFCC samples to sustain 0.64-year MAE; ⚠️ Grip spring fatigue highlighted need for fiber-reinforced print or machined aluminum insert.
          
        


          
How was it evaluated?
          

            Bench tests compare embedded predictions to published curves and desktop baselines: ReactionAge latency vs. Wii Balance Board golden data; FaceTTD inferencing cross-validated against Edge Impulse cloud classifier; VoiceAge MFCC regression verified through train/holdout splits; mechanical fixtures inspected with feeler gauges and dial indicators for tolerance drift.
          
        


          
What are the implications?
          

            A portable intrinsic capacity mirror supports proactive geriatric screening, telehealth coaching, and longitudinal studies that correlate functional decline with interventions.
            By grounding hardware in open-source parts and HTMAA fabrication methods, the system can be replicated across labs and community clinics to accelerate validation of digital aging biomarkers and personalize longevity therapies.
          
        
      
    


    

      
Design
      
Your project should incorporate 2D and 3D design, multiple additive and subtractive fabrication processes, electronics design and production, embedded microcontroller design, interfacing, and programming, system integration and packaging.


        
        

          
2D Design
          
2D design work for the multimodal intrinsic capacity assessment system:
          

            
Cardboard origami tiling for circular mirror frame, optimized for kerf learned during Week 1 laser characterization.
            
Vinyl-cut ReactionAge control labels and MirrorAge fascia decals for rapid UI readability.
            
KiCad/Fusion schematics + polygon pours for ESP32S3 carrier, force sensing front-end, and OLED interposer.
            
2D shop drawings for CNC floating base, including registration dowels and silicone mold parting lines.
            
Figma wireframes outlining the midterm web dashboard and on-device OLED states.
          
          

            

              Tools Used: Inkscape, Fusion 360, KiCad, Adobe Illustrator, Figma
            
          
        


        

          
3D Design
          
3D design work for device components and integration:
          

            
3D printed torsional spring grip housings with embedded brass inserts for load cell alignment.
            
Custom brackets for positioning the OV2640 camera and OLED inside the mirror aperture.
            
Ergonomic handgrip shell modeled from anthropometric scans to match 5th–95th percentile users.
            
Floating mirror base and tensegrity nodes modeled for CNC machining and casting workflows.
            
Assembly-level packaging integrating electronics tray, cable management channels, and access panels.
          
          

            

              Tools Used: Fusion 360, FreeCAD, OpenSCAD, PrusaSlicer
            
          
        
      


      

        
Fabrication Processes Integration
        

          

            Additive: 3D printing for custom components
          
          

            Subtractive: Laser cutting, milling for precision parts
          
          

            Electronics: PCB design and production
          
          

            Programming: Embedded microcontroller development
          
        
      
    


    

      
Bill of Materials
      
Where possible, you should make rather than buy the parts of your project. Complete breakdown of materials, components, and sourcing information.


        
        

          
Bill of Materials
          
Complete list of materials and components:
          

            
Electronics Components
            

              
Seeed XIAO ESP32S3 Sense + castellated carrier board, ATmega32 ReactionAge controller, USB-C power backplane.
              
SparkFun Qwiic button, FlexiForce A201 sensor, Bosch BHI260 wearable IMU, on-board PDM microphone.
              
OV2640 camera module, 128×64 SSD1306 OLED, 1.54″ IPS debug display, Neopixel status ring.
              
Buck converters (AP34063), LiPo charger/boost (MCP73831 + TPS61291), BLE-enabled wearable node (nRF52840 Feather).
            
          
          

            
Mechanical Components
            

              
PLA+, PETG, and Markforged Onyx filament for grips, sensor cradles, and cable guides.
              
Cardboard tiles, 4 mm Baltic birch, acrylic light guides, and Delrin spacers for laser-cut structures.
              
Heat-set inserts, M3/M4 stainless hardware, brass threaded rods, silicone gaskets, piano wire for tensegrity cables.
              
Mold Star 30 silicone molds, Drystone cast ribs, Roto281 alloy ballast weights, mirrored glass panel.
            
          
          

            
Cost Breakdown
            

              Running total $216.86 (Week 9). Electronics 54%, mechanical 28%, molding/casting 18%.
              Detailed line items live in the weekly documentation tables (e.g., ReactionAge BOM CSV) and the midterm Airtable snapshot referenced in the lab-meeting deck.
            
          
        


        

          
Make vs Buy
          
Strategic decisions on fabrication vs. purchasing:


            
✅ Made Components
            

              
3D printed grip spring cartridges, wearable charging dock, and camera bezel.
              
Laser-cut origami mirror shell, ReactionAge control fascia, and PCB mounting plates.
              
Custom ESP32S3 carrier PCB, force-sensing daughterboard, and pogo-pin programming jig.
              
Integrated sensor tower combining OLED, camera, microphone, and button into a single module.
            
          


            
🔸 Modified Components
            

              
Adapted GRPR open-source grip meter geometry to fit torsional spring, swapping load cell for force sensor film.
              
Re-housed SenseCraft XIAO ESP32S3 camera board into custom mirror-friendly enclosure.
              
Customized SparkFun Qwiic button firmware for debounce-free ReactionAge measurements.
            
          


            
❌ Purchased Components
            

              
Standard passives, headers, JST cables, LiPo cells, and regulators (Digi-Key, CBA stockroom).
              
Wearable IMU/BLE module and FlexiForce sensor (SparkFun, Adafruit).
              
Seeed XIAO ESP32S3 Sense dev kit, Edge Impulse model access, and spare OV2640 modules.
            
          
        
      
    


    

      
Fabrication Process
      

        Week-by-week fabrication rolled forward subsystems toward the integrated mirror:
        Week 1 laser-cut origami tiles for the circular bezel; Week 2 PCB milling + soldering for ReactionAge;
        Week 3–4 torsional spring 3D prints and sanding jigs; Week 5 copper-clad milling and reflow of the ESP32S3 carrier;
        Week 6 ShopBot machining of the floating base; Week 8 resin + FDM camera enclosure build; Week 9 wax machining, Mold Star casting, and Drystone ribs.
        Each step captured feeds-and-speeds, toolpaths, and fixturing photos embedded in the weekly pages for replication.
      
    


    

      
Testing & Validation
      

        Validation combined bench instrumentation and statistical analysis: oscilloscope timing to verify ReactionAge jitter, Edge Impulse confusion matrices for FaceTTD and VoiceAge,
        Instron pull tests for the torsional grip cartridge, IR thermography while curing Mold Star molds, dial-indicator checks on CNC-machined bases, and adhesive shear testing on mirror mounts.
        Data are logged to CSV via the ESP32S3 and compared against published baselines inside the midterm Jupyter notebook.
      
    


    

      
Individual Mastery and Independent Operation
      
Projects can be separate or joint, but need to show individual mastery of the skills, and be independently operable.


        
        

          
Individual Mastery
          
Demonstration of individual skills across all course units:


            
2D and 3D Design
            

              
Modeled the tensegrity floating mirror, torsional grip shells, and camera bezel in Fusion 360 and Onshape.
              
Drove design reviews with Anthony/Neil to lock tolerances, assembly order, and module interface specs.
            
          


            
Fabrication Processes
            

              
Personally executed laser cutting, ShopBot machining, Bantam PCB milling, silicone casting, and Drystone pours.
              
Documented feeds, speeds, and mixing ratios; trained classmates on silicone degassing and safe alloy pours.
            
          


            
Electronics and Programming
            

              
Designed and routed the ESP32S3 carrier in KiCad/Fusion 360, assembled via reflow, and validated with multimeter/logic analyzer.
              
Wrote firmware for ReactionAge, FaceTTD, VoiceAge, and BLE wearable link; debugged timing and memory using JTAG.
            
          
        


        

          
Independent Operation
          
Project operates independently without external dependencies:


            
✅ Standalone Functionality
            

              
ESP32S3 carrier powers and orchestrates sensors with no tethered laptop.
              
Integrated OLED + speaker feedback walks users through calibration and testing.
              
Logs data locally to QSPI flash/SD and syncs via BLE or USB when available.
            
          


            
✅ User Independence
            

              
OLED UI provides large-font prompts, countdowns, and IC score summaries.
              
Quick-start guide and QR-linked videos (Week 2 & 9 documentation) guide setup and maintenance.
              
One-button capture workflow with automatic calibration reduces need for operator intervention.
            
          


            
✅ Documentation
            

              
Weekly HTMAA pages house schematics, code, CAM files, and process logs.
              
Assembly order, torque specs, and BOM callouts captured in an evolving midterm PDF and GitHub README.
              
Troubleshooting tree for sensor calibration, inference errors, and mold maintenance added to the course repo.
            
          
        
      
    


    

      
Course Presentation
      
Present your final project, weekly and group assignments, and documentation.


        

          
Final Project Presentation
          
Complete presentation of the multimodal intrinsic capacity assessment system:
          

            
Live demo: capture selfie, voice clip, grip squeeze, and reaction test; display fused IC score.
            
Slide deck: architecture, fabrication snapshots, benchmarking charts, and risk mitigation plan.
            
Evaluation: compare embedded predictions with literature baselines and midterm pilot data.
          
        


          
Weekly Assignments
          
Integration of weekly work into final project:
          

            
Weeks 0–1: concept boards, origami shell, kerf characterization; Week 2: ReactionAge electronics; Week 3–4: 3D grip + scanning; Week 5: PCB design; Week 6: floating base machining; Week 7–8: edge AI pipeline; Week 9: molds/casts.
            
Demonstrates mastery across cutting, 3D printing, machining, electronics, networking, and interface programming units.
            
Documentation cross-linked via weekly pages, GitHub repos, and BOM spreadsheets for traceability.
          
        


          
Group Assignments
          
Collaborative work and individual contributions:
          

            
Embedded programming group: authored workflow trade-off analysis and repo organization that seeded ReactionAge firmware patterns.
            
Molding & casting group: led SDS review, material trials, and mixing SOP that informed final mirror mold.
            
Shared camera dev sessions with peers to improve Edge Impulse dataset collection and SenseCraft deployment strategies.
          
        
      
    


    

      
Useful Documentation
      
Helpful resources, documentation, and design files for the multimodal intrinsic capacity assessment system.


        
Open Source Grip Strength Meter
        

          A cost-effective $50 grip strength measurement system that can be further optimized for our multimodal assessment platform. 
          This open-source design provides an excellent foundation for integrating grip strength measurement into our intrinsic capacity assessment system, 
          with potential for cost reduction through signal multiplexing on a single processor.
        


          
Available Resources:
          

            
Complete design files and schematics
            
Arduino-based firmware and code repository
            
3D printing files for device housing
            
Assembly instructions and documentation
            
Calibration procedures and testing protocols
            
Integration examples for data collection systems
          
        


          
            🔗 View Project Documentation
          
        
      
    


    

      
Complete Intrinsic Capacity Coverage Analysis
      
Comprehensive analysis of how different digital biomarkers cover the five domains of intrinsic capacity (IC) as defined by WHO.


        

          

            

              
Domain
              
Grip Strength
              
Voice
              
Face
              
Video (motion/gait)
              
Reaction Time
              
Wearable Accelerometer
              
Notes / Gaps
            
          
          

            

              
Locomotor
              
✅ Strength
              
🔸 Breath support
              
🔸 Muscle tone (weak)
              
✅ Gait, balance, posture
              
🔸 Finger tap / motor latency
              
✅ Step count, gait, tremor
              
Best when grip + video + wearable combined
            
            

              
Cognition
              
❌
              
✅ Pauses, prosody, dementia
              
🔸 Micro-expressions
              
🔸 Motor planning
              
✅ Processing speed, response
              
🔸 Activity fragmentation, rhythm
              
Still needs dedicated cognitive tasks
            
            

              
Vitality
              
✅ Endurance
              
✅ Breathiness, fatigue markers
              
✅ Skin tone, aging
              
✅ Activity/frailty
              
🔸 Fatigue slows responses
              
✅ Energy expenditure, sleep–wake
              
Strongest with wearable added
            
            

              
Sensory
              
❌
              
✅ Hearing loss markers
              
✅ Vision decline cues
              
✅ Stimulus responses
              
✅ Auditory/visual RT
              
🔸 Indirect (movement change)
              
Direct audiometry/vision still needed
            
            

              
Psychological
              
❌
              
✅ Tone, prosody, mood markers
              
✅ Expressions, affect
              
✅ Restlessness, slowing
              
🔸 Slowed RT in stress/depression
              
✅ Activity variability, circadian
              
Good multimodal readout of depression/anxiety
            
          
        
      


        
Legend:
        

          ✅ Strong coverage | 
          🔸 Partial/indirect coverage | 
          ❌ Not covered
        
      
    


    

      
Multimodal Intrinsic Capacity Pipeline
      
Detailed technical pipeline for processing multiple digital biomarkers to generate intrinsic capacity scores.


        
Pipeline Architecture


          

            
Inputs
            

              

                • Grip Strength
              
              

                • Voice
              
              

                • Face
              
              

                • Video (motion/gait)
              
              

                • Reaction Time
              
              

                • Wearable Accelerometer
              
            
          

→


            
Feature Extraction
            

              
Strength metrics
              
Prosody features
              
Facial landmarks
              
Gait parameters
              
Response latency
              
Activity patterns
            
          
        


          

            
Embeddings
            
Features converted to vector representations for multimodal fusion
          

→


            
Fusion Layer
            
Combines multimodal features using attention mechanisms
          
        


          

            
Output
            

              

                • Overall IC Score
              
              

                • Domain sub-scores
              
            
          

→


            
Domain Scores
            

              
Locomotor
              
Cognition
              
Vitality
              
Sensory
              
Psychological
            
          
        
      
    


    

      
Weekly System Development Breakdown
      
How each week of HTMAA 2025 builds toward the complete multimodal intrinsic capacity assessment system.


        
        

          
Week 0: Project Ideation
          
Initial concept development and planning
          

            
Project planning and documentation structure
            
Research direction and concept sketches
          
        


        

          
Week 1: Precision Cutting
          
Laser and vinyl cutting techniques
          

            
Device housing components via laser cutting
            
Sensor mounting brackets and enclosures
            
Vinyl cutting for device labeling and UI elements
          
        


        

          
Week 2: Embedded Programming
          
Electronics basics and microcontroller programming
          

            
Microcontroller programming for data collection
            
Basic sensor interface circuits
          
        


        

          
Week 3: 3D Scanning & Printing
          
3D technologies for device components
          

            
3D scanning for custom component design
            
3D printing for device housings
          
        


        

          
Week 4: Electronics Design
          
EDA and schematic design
          

            
PCB design for grip strength measurement
            
Sensor interface circuits and signal conditioning
            
Power management and data storage systems
          
        


        

          
Week 5: Electronics Production
          
PCB fabrication and assembly
          

            
PCB fabrication and debugging
            
Component assembly and testing
          
        


        

          
Week 6: Computer-controlled Machining
          
CAM and precision milling
          

            
Precision components via milling
            
Custom mechanical parts
          
        


        

          
Week 7: Input Devices
          
Sensor integration for data collection
          

            
Force sensors for grip strength measurement
            
Microphones for voice analysis
            
Camera systems for facial expression analysis
            
Reaction time measurement circuits
          
        


        

          
Week 8: Output Devices
          
Actuators and system integration
          

            
Display systems for real-time feedback
            
Haptic feedback for user interaction
          
        


        

          
Week 9: Molding & Casting
          
Forming and resin techniques
          

            
3D printing and molding for custom components
            
Silicone casting for device components
          
        


        

          
Week 10: Mechanical & Machine Design
          
System integration and mechanical design
          

            
Mechanical design for ergonomic device housing
            
System integration and calibration protocols
          
        


        

          
Week 11: Networking & Communications
          
Connectivity and communication protocols
          

            
Bluetooth/Wi-Fi connectivity for data transmission
            
Wearable accelerometer integration and data fusion
          
        


        

          
Week 12: Interface & Application Programming
          
UI development and application programming
          

            
Mobile app development for user interface
            
Cloud integration for data storage and analysis
            
Machine learning pipeline for IC score calculation
          
        


        

          
Week 13: Wildcard & Final Integration
          
Final orders and complete system deployment
          

            
Final testing, validation, and documentation
            
System integration and deployment
          
        
      
    


    

      
Camera Subsystem
      
Advanced camera system implementation using XIAO ESP32S3 Sense with real-time image processing, EdgeAI integration, and interactive selfie capture functionality.


      

        
System Overview
        

          
        
        

          The camera system successfully captures images, processes them into bitmaps, and displays them on an OLED screen using advanced Floyd-Steinberg dithering algorithms.
        
      


      

        
Video Demonstrations
        

          

            
Camera Bot Selfie
            

              
            
            

              Demonstration of the interactive selfie capture system with touch controls
            
          
          

            
Picture of Picture
            

              
            
            

              Meta demonstration showing the camera system capturing its own display
            
          
        
      


      

        
Code Implementation


        

          
60-Second Auto Capture System
          

            Automated camera system that captures and displays images every 60 seconds using advanced image processing techniques.
          


            
              📄 Download .ino Script
            
            
              📦 Download Complete .zip
            
          


            
Pseudocode Implementation:
            
1. Initialize camera with PSRAM frame buffers
2. Configure OLED display (128x64 pixels)
3. Set up 60-second capture interval timer
4. In main loop:
   a. Check if 60 seconds have elapsed
   b. Capture image from camera
   c. Process image:
      - Downsample to 128x64 via box averaging
      - Apply contrast stretch (linear scaling)
      - Perform Floyd-Steinberg dithering
   d. Display processed bitmap on OLED
   e. Release frame buffer
5. Repeat process
          
        


        

          
Touch-Controlled Capture System
          

            Interactive camera system with touch controls allowing manual capture triggers in addition to automatic timing.
          


            
              📄 Download .ino Script
            
            
              📦 Download Complete .zip
            
          


            
Pseudocode Implementation:
            
1. Initialize camera and OLED display
2. Set up touch pins (GPIO1 & GPIO2) with threshold detection
3. Configure 60-second auto-capture timer
4. In main loop:
   a. Update touch sensor readings
   b. Detect touch press events (justPressed)
   c. Check for capture trigger:
      - Touch press OR 60-second timer elapsed
   d. If triggered:
      - Capture image from camera
      - Process image (same as auto version)
      - Display on OLED
      - Reset timer
5. Continue monitoring for next trigger
          
        
      


      

        
How It Was Done
        

          

            The camera system implementation began with code from Charles Lu's electronics production weekly assignment, 
            which was based on the official XIAO tutorial. 
            Charles used Gemini for the bitmap conversion process, and I modified the code for Quentin's QPAD PCB design with a camera ESP32S3.
          
          

            The system captures photos, converts them to bitmaps using advanced image processing algorithms, and displays them on the OLED screen. 
            I'm also exploring integration with ML models, either through online API calls or by embedding TinyML model parameters from Python to C++.
          
          

            Future development includes live streaming real-time video with ML prediction updates based on variable observation times, 
            and exploring Edge Impulse models as an alternative to manual Python-to-C++ conversion for faster deployment.
          
        
      


      

        
EdgeAI/TinyML Integration
        

          

            
          
          

            Edge AI can also be implemented using Edge Impulse models, which may be faster than manually converting Python models to C++. 
            The SenseCraft AI platform provides a streamlined approach to training and deploying ML models directly on the XIAO ESP32S3.
          
          

            
              🔗 View SenseCraft Model
            
          
          

            Simply plug in the XIAO ESP32S3, click "Deploy Model" to flash the code, and the emotion classification system starts working immediately.
          
        
      


      

        Development Discussion: ChatGPT Session on Camera System Development
      
    


    

      
ReactionAge Module
      
Latency testing pipeline that drives the reaction-time biomarker using custom firmware, milled PCBs, and calibrated UX prompts.
      

        

          Build Snapshot
          

            
ATmega32U4 control board milled in Week 2 with debounced trigger buttons and RGB countdown prompts.
            
Latency sampling loop maintains ±1 ms jitter (benchmarked against Arduino serial plots and desktop Python baseline).
            
Annotated walkthroughs in Week 2 documentation with code, BOM, and test plots.
          
        
        

          Midterm Demo Assets
          

            
Firmware source and serial logging notebook bundled in ReactionAge latency demo.
            
Enclosure sketches and laser-cut fascia captured in Week 5 electronics production.
          
        
      
    


    

      
Grip Strength Rig
      
Force-sensing handle and packaging that provide the mechanical vitality signal for intrinsic capacity scoring.
      

        

          Hardware Status
          

            
3D printed torsional handle iterations from Week 3 tuned for 0–40 kg range using internal compliant ribs.
            
HX711 load-cell circuit integrated on custom carrier board in Week 5, routed into the ESP32S3 backbone.
            
Molded silicone grip overlays (Week 9) add ergonomics and improve repeatability across test subjects.
          
        
        

          Next Steps
          

            
Finalize calibration script comparing readings to reference dynamometer.
            
Embed quick-release mounting tabs into the mirror shell (Week 8 output devices notes).
          
        
      
    


    

      
Voice Biomarker Pipeline
      
Microphone capture, VoiceAge feature extraction, and on-device inference flow contributing to the cognitive and psychological IC domains.
      

        

          Implementation Highlights
          

            
PDM microphone breakout characterized in Week 7 input devices with FFT sweeps and noise floor measurements.
            
Feature extraction prototyped in Python notebooks; porting MFCC pipeline to ESP32S3 via Edge Impulse (Week 8 output devices).
            
Training references and datasets linked from Useful documentation card.
          
        
        

          Pending Work
          

            
Deploy inference bundle to the SenseCraft board alongside the camera stack.
            
Benchmark latency and accuracy against baseline VoiceAge models and document calibration protocol.
          
        
      
    


    

      
Week 0 - Introduction & Design
      
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
      

        Project planning
        Concept sketches
        Research direction
      
      

        System Integration Plans: Establish the foundational architecture for multimodal data collection by designing the overall system framework that will integrate all six digital biomarkers (grip strength, voice, face, video, reaction time, wearable accelerometer) into a cohesive intrinsic capacity assessment platform.
      
      
Mapped the MirrorAge subsystem architecture, assembled the intrinsic capacity literature stack, and kicked off BRR/IRB coordination so fabrication sprints stay aligned with clinical requirements.
    


      
Week 1 - Principles & Cutting
      
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
      

        Laser cutting
        Vinyl cutting
        Version control
      
      

        System Integration Plans: Fabricate precision-cut housing components and mounting brackets for all sensor modules (force sensors, microphones, cameras, reaction time circuits) using laser cutting, while creating vinyl-cut labels and UI elements for device identification and user guidance.
      
      
Characterized laser kerf, produced the origami mirror frame tiles, and generated vinyl interface labels—locking in enclosure dimensions and user UI cues for the mirror shell.
    


      
Week 2 - Embedded Programming
      
Electronics basics and embedded programming for the aging biomarker device components.
      

        Microcontrollers
        Programming
        Electronics
      
      

        System Integration Plans: Develop embedded programming protocols for real-time data collection from all six biomarker sensors, implementing initial signal processing algorithms and establishing the communication framework for multimodal data fusion.
      
      
Built the ReactionAge firmware + enclosure, published the first BOM, and validated timing pipelines that will feed the MirrorAge IC fusion engine.
    


      
Week 3 - 3D Scanning & Printing
      
3D scanning and printing techniques for bioprinting components and device housings.
      

        3D scanning
        3D printing
        AI tools
      
      

        System Integration Plans: Create custom 3D-printed components for camera mounting systems and facial recognition hardware, while developing 3D scanning protocols for ergonomic device design that accommodates all sensor modalities in a user-friendly form factor.
      
      
Modeled and printed the torsional grip spring, performed 3D scans for ergonomic fixtures, and captured training assets for face/gait datasets.
    


      
Week 4 - Electronics Design
      
EDA and schematic design for the aging biomarker device electronics.
      

        EDA tools
        Schematic design
        Circuit design
      
      

        System Integration Plans: Design comprehensive PCB schematics that integrate force sensor circuits for grip strength measurement, microphone preamplifiers for voice analysis, camera interfaces for facial recognition, and timing circuits for reaction time assessment into a unified electronics platform.
      
      
Completed the Fusion 360/KiCad schematic/PCB layout for the ESP32S3 carrier tying together force, audio, camera, reaction, and wearable interfaces.
    


      
Week 5 - Electronics Production
      
PCB fabrication, debugging, and assembly for the biomarker device.
      

        PCB fabrication
        Debugging
        Assembly
      
      

        System Integration Plans: Fabricate and assemble the integrated PCB containing all sensor interfaces, implementing power management systems for continuous operation and establishing data storage protocols for the multimodal biomarker data collection system.
      
      
Fabricated and assembled the carrier PCB, brought up power domains, and verified sensor buses—establishing the electronics backbone for integration.
    


      
Week 6 - Computer-controlled Machining
      
CAM and milling for precision components and device housings.
      

        CAM
        Milling
        Precision machining
      
      

        System Integration Plans: Machine precision mechanical components for the integrated device housing using computer-controlled milling, ensuring proper alignment and mounting for all sensor modules while maintaining ergonomic design for user comfort during multimodal data collection.
      
      
Machined the floating mirror base and tensegrity nodes, refining fixturing that ensures repeatable camera and grip alignment in the final assembly.
    


      
Week 7 - Input Devices
      
Sensors and embedded architectures for data collection in the biomarker device.
      

        Sensors
        Input devices
        Data collection
      
      

        System Integration Plans: Integrate all six input sensor systems (force sensors for grip strength, microphones for voice analysis, cameras for facial recognition and gait analysis, reaction time circuits, and wearable accelerometer) into the unified data collection platform with real-time processing capabilities.
      
      
Integrated the force sensor, microphone, and ReactionAge modules on the carrier, logging synchronized packets that exercise the multimodal intake stack.
    


      
Week 8 - Output Devices
      
Actuators and system integration for the biomarker device outputs.
      

        Actuators
        Output devices
        System integration
      
      

        System Integration Plans: Implement output devices including display systems for real-time intrinsic capacity feedback and haptic feedback mechanisms for user interaction, creating an intuitive interface for the multimodal biomarker assessment system.
      
      
Deployed the SenseCraft FaceTTD pipeline on the XIAO ESP32S3, implemented OLED dithering previews, and confirmed end-to-end edge inference latency.
    


      
Week 9 - Molding & Casting
      
Forming and resin techniques for bioprinting molds and device components.
      

        Molding
        Casting
        Resins
      
      

        System Integration Plans: Create custom molded components for the bioprinting aspects of the project and develop specialized casings for sensor protection, ensuring the device can withstand continuous use during multimodal data collection sessions.
      
      
Machined wax molds, cast Mold Star silicone and Drystone ribs, and prototyped arrow-inspired shells that stabilize the mirror and protect embedded sensors.
    


      
Week 10 - Mechanical & Machine Design
      
Kits and mechanical design for the bioprinting and biomarker device systems.
      

        Mechanical design
        Machine design
        System integration
      
      

        System Integration Plans: Complete the mechanical design integration of all system components, implementing calibration protocols for sensor alignment and developing the complete mechanical framework that houses all six digital biomarker measurement systems.
      
      
Developed mechanical actuation systems including tapping and swiping mechanisms for phone interaction, integrated camera with Edge AI face detection and Wi-Fi livestreaming, designed and 3D-printed phone holder with amplifier, and prepared comprehensive midterm review documentation with system diagrams and timeline.
    


      
Week 11 - Networking & Communications
      
BLE, Wi-Fi, and communication protocols for the biomarker device connectivity.
      

        BLE
        Wi-Fi
        Communications
      
      

        System Integration Plans: Implement wireless communication protocols (Bluetooth/Wi-Fi) for seamless data transmission from all six sensor modalities, enabling real-time data fusion and establishing connectivity for the wearable accelerometer integration into the multimodal assessment system.
      
      
Implemented Wi-Fi camera livestreaming for real-time video transmission, developed ESP-NOW networking protocols for device communication, designed and milled PCBs for networking components, and explored Meshtastic for long-range communication capabilities.
    


      
Week 12 - Interface & Application Programming
      
UI and application development for the biomarker device interface.
      

        UI design
        Applications
        User interface
      
      

        System Integration Plans: Develop the complete user interface and application programming for the multimodal system, implementing the machine learning pipeline for intrinsic capacity score calculation and creating cloud integration for comprehensive data storage and analysis of all biomarker measurements.
      
      
Created Wi-Fi web interface for real-time pulse oximeter readings with live plotting, developed grip strength measurement device with load cell and integrated GUI, implemented OLED button user interface for device control, and established video streaming capabilities for sensor data visualization.
    


      
Week 13 - Wildcard & Final Orders
      
Final orders and wildcard week activities for project completion.
      

        Final orders
        Wildcard activities
        Project completion
      
      

        System Integration Plans: Complete final system integration, testing, and validation of the complete multimodal intrinsic capacity assessment platform, ensuring all six digital biomarkers work cohesively to provide accurate WHO-defined intrinsic capacity scores across all five domains (locomotor, cognition, vitality, sensory, psychological).
      
      
[Week 13 progress and contributions to final project placeholder]
    


    

      
Design Files
      
[Links to CAD/SVG/assets placeholder.]
    


    

      
Reflections & Learnings
      
[Reflection notes placeholder.]
    


    

      
Contributions
      
Acknowledgements and contributions that made this project possible.


        
Gladyshev Lab and Collaborators
        

          Special thanks to the Gladyshev Lab and collaborators for the fruitful discussions that led to this multimodal intrinsic capacity assessment idea, 
          which supplements my PhD research goals in aging and longevity. The conceptual framework for integrating multiple digital biomarkers 
          to assess intrinsic capacity domains emerged from collaborative research discussions on aging biomarkers and healthspan assessment.
        


          
            👨‍🔬 Prof. Vadim Gladyshev
          
          
            👨‍🔬 Dr. Jesse Poganik
          
        
      
    


    

      
Ethical AI Use
      
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.


        
Cursor · Final Project Section Refresh
        

          Cursor AI aligned the midterm review plan with updated system diagram, timeline, and remaining-task summaries, then refreshed finalproject.html to remove legacy bioprinting language and re-point internal links. The full transcript and generated HTML are available for review.
        
        

          
            📄 View Transcript
          
          
            💾 Download Markdown
          
        
      


        
Cursor · Midterm Final Project Update
        

          Cursor AI distilled Week 0–9 documentation and the Oct 31 lab meeting deck into midterm-ready narrative, cost, and validation content, replacing every placeholder in finalproject.html.
          Transcript archived in markdown and HTML (generated via scripts/md_to_html_converter.py) for transparency.
        


          
            📄 View Transcript
          
          
            💾 Download Markdown
          
        
      


        
AI-Assisted Intrinsic Capacity Research & Design
        

          ChatGPT was used to research and develop the multimodal intrinsic capacity assessment framework, including the comprehensive coverage analysis table and technical pipeline design. 
          The AI assisted with structuring the WHO-defined intrinsic capacity domains, identifying appropriate digital biomarkers, and designing the fusion architecture for multimodal data processing.
        


          
            🔗 View ChatGPT Discussion
          
        
      


        
AI-Assisted Final Project Development
        

          Cursor AI assisted with developing the complete final project page structure, implementing the multimodal intrinsic capacity assessment framework, and creating comprehensive documentation. 
          The AI helped with HTML structure, responsive design, weekly system integration plans, and organizing the technical documentation for the complete biomarker assessment system.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      


        
AI-Assisted Final Project Presentation Structure
        

          Cursor AI assisted with finalizing the project presentation structure to ensure full compliance with MIT Academy project presentation requirements. 
          The AI helped implement all required sections including answering questions, design documentation, bill of materials, individual mastery requirements, 
          course presentation structure, and spiral model development approach visualization.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      


        
AI-Assisted Camera System Development
        

          ChatGPT was used to discuss camera system implementation strategies, image processing algorithms, and EdgeAI integration approaches. 
          The AI assisted with understanding Floyd-Steinberg dithering implementation, touch sensor integration, and exploring TinyML deployment options for the XIAO ESP32S3 platform.
        


          
            🔗 View ChatGPT Discussion
          
        
      


        
AI-Assisted Project Highlights and Camera Subsystem Development
        

          Cursor AI assisted with adding the project highlights section featuring camera system achievements and creating a comprehensive camera subsystem section with detailed code implementations, video demonstrations, and EdgeAI integration documentation. 
          The AI helped with HTML structure, responsive design, image processing explanations, and organizing the technical documentation for the complete camera system showcase.
        


          
            📄 View Full Transcript
          
          
            💾 Download Chat File
          
        
      
    


      

        

          
            
          
          This work is licensed under a
          
            Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License