Final Project

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.

• 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.

• 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.

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.

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.

• 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.

• 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.

• 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.

• 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.

• 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.

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.

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.

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.

Bill of Materials

Complete list of materials and components:

Make vs Buy

Strategic decisions on fabrication vs. purchasing:

Individual Mastery

Demonstration of individual skills across all course units:

Independent Operation

Project operates independently without external dependencies:

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.

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

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

60-Second Auto Capture System

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

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.

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

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.

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.

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

• 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.

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

• 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.

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

• 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.

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