# Add project highlights and camera subsystem details
_Exported on 10/18/2025 at 03:23:28 EDT from Cursor (1.7.44)_
---
**User**
add a project highlights section to the final project page finalproject.html (formatted like the weekly highlights sections on any weekX.html page)
the row should be Camera Works! camera-worked.jpg, EdgeAI Works! edgeAI-works.jpeg, Selfie Capture! toddler-botselfie.gif
each should link to a camera subsystem subsection, make this new subsection, include the image camera-worked.jpg and the videos camerabot-selfie.mp4, picture-of-picture.mp4. add a code subsubsection for camera_60sec.ino/camera_60sec.zip and camera_button.ino/camera_button.zip and for each put a link to download .ino script or full .zip code, explain in pseudocode how it works. add a link to chatgpt session here: @https://chatgpt.com/share/68f03966-d978-8008-b318-1fd634904e25 (also put it in ethical ai use section for final project)
refine these texts into a how it was done subsubsection: Captures the photo, makes it a bitmap, and displays it yes
Also trying to find out how to either get/post to ml model online or tinyml embed model parameters from python to c
I’ll also try to live stream realtime video and have the ML prediction update based on a variable observation time
I started with the code on Charles Lu’s electronics production weekly assignment @https://fab.cba.mit.edu/classes/863.25/people/CharlesLu/ (which is based on the official XIAO tutorial @https://wiki.seeedstudio.com/xiao_esp32s3_camera_usage/ , and he used Gemini for the bitmap conversion process) and modified it for Quentin’s QPAD PCB design with a camera ESP32S3 on it
add a new subsubsection to camera system on edgeAI/tinyML
The edge AI can also be done with Edge Impulse models, looking through them too — might be faster to just train an ML model with the same dataset on their platform instead of switching python to c and all that confusion. add image @edgeAI-works.jpeg and link to here @https://sensecraft.seeed.cc/ai/view-model/60061-emotion-classification?tab=public (just plug in the xiao and click deploy model to flash the code and it starts working)
for each image and video, make sure there is a caption and description
refine the text to be professional and succinct without losing any key details
make sure the added content is aesthetically pleasing and well-formatted
---
**Cursor**
I'll help you add a project highlights section to the final project page and create the camera subsystem section with all the requested content. Let me start by examining the current final project page and the weekly pages to understand the formatting structure.
Let me read the weekly pages to understand the highlights format:
Now let me check the camera files to understand what content I need to work with:
Now let me create the todo list and start implementing the changes:
Now I'll start by adding the project highlights section to the final project page. Let me first find where to insert it:
I can see there's already a Project Highlights section. Let me add the camera highlights row to it. I'll insert it after the existing content but before the Vision Board section:
```html
Final Project · HTMAA 2025 · Saleem A. Al Dajani
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
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.
[Project introduction placeholder - describing the bioprinting and aging biomarker device project.]
[Optional project details placeholder]
Project Goals
[Goal 1] [Details]
[Goal 2] [Details]
[Goal 3] [Details]
Timeline & Milestones
[Milestone 1] [Details]
[Milestone 2] [Details]
Tools & Materials
[Tool/Material 1]
[Tool/Material 2]
Answering Questions
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.
What does it do?
[Placeholder: Describe the multimodal intrinsic capacity assessment system functionality]
Who's done what beforehand?
[Placeholder: Literature review and prior work in aging biomarkers and intrinsic capacity assessment]
What sources did you use?
[Placeholder: Research papers, open-source projects, and reference materials]
What did you design?
[Placeholder: Custom hardware, software, and integration systems designed]
What materials and components were used?
[Placeholder: Complete list of materials, sensors, microcontrollers, and components]
Where did they come from?
[Placeholder: Suppliers, vendors, and sourcing information]
How much did they cost?
[Placeholder: Detailed cost breakdown and budget analysis]
What parts and systems were made?
[Placeholder: Custom fabricated parts, 3D printed components, and assembled systems]
What tools and processes were used?
[Placeholder: Laser cutting, 3D printing, PCB fabrication, programming, and testing processes]
What questions were answered?
[Placeholder: Research questions addressed and hypotheses tested]
What worked? What didn't?
[Placeholder: Successes, challenges, and lessons learned]
How was it evaluated?
[Placeholder: Testing protocols, validation methods, and evaluation criteria]
What are the implications?
[Placeholder: Impact on aging research, clinical applications, and future development]
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:
[Placeholder: Laser cutting designs for device housing components]
[Placeholder: Vinyl cutting designs for labels and UI elements]
[Placeholder: PCB layout designs and schematics]
[Placeholder: 2D technical drawings and assembly guides]
[Placeholder: User interface mockups and wireframes]
[Placeholder: Skills demonstration across all units]
[Placeholder: Progressive development documentation]
Group Assignments
Collaborative work and individual contributions:
[Placeholder: Group project contributions]
[Placeholder: Individual role and responsibilities]
[Placeholder: Collaborative learning outcomes]
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.
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
Bioprinting molds and 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
Week 0 - Introduction & Design
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
Project planningConcept sketchesResearch 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.
[Week 0 progress and contributions to final project placeholder]
Week 1 - Principles & Cutting
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
Laser cuttingVinyl cuttingVersion 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.
[Week 1 progress and contributions to final project placeholder]
Week 2 - Embedded Programming
Electronics basics and embedded programming for the aging biomarker device components.
MicrocontrollersProgrammingElectronics
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.
[Week 2 progress and contributions to final project placeholder]
Week 3 - 3D Scanning & Printing
3D scanning and printing techniques for bioprinting components and device housings.
3D scanning3D printingAI 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.
[Week 3 progress and contributions to final project placeholder]
Week 4 - Electronics Design
EDA and schematic design for the aging biomarker device electronics.
EDA toolsSchematic designCircuit 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.
[Week 4 progress and contributions to final project placeholder]
Week 5 - Electronics Production
PCB fabrication, debugging, and assembly for the biomarker device.
PCB fabricationDebuggingAssembly
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.
[Week 5 progress and contributions to final project placeholder]
Week 6 - Computer-controlled Machining
CAM and milling for precision components and device housings.
CAMMillingPrecision 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.
[Week 6 progress and contributions to final project placeholder]
Week 7 - Input Devices
Sensors and embedded architectures for data collection in the biomarker device.
SensorsInput devicesData 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.
[Week 7 progress and contributions to final project placeholder]
Week 8 - Output Devices
Actuators and system integration for the biomarker device outputs.
ActuatorsOutput devicesSystem 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.
[Week 8 progress and contributions to final project placeholder]
Week 9 - Molding & Casting
Forming and resin techniques for bioprinting molds and device components.
MoldingCastingResins
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.
[Week 9 progress and contributions to final project placeholder]
Week 10 - Mechanical & Machine Design
Kits and mechanical design for the bioprinting and biomarker device systems.
Mechanical designMachine designSystem 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.
BLEWi-FiCommunications
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 designApplicationsUser 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 ordersWildcard activitiesProject 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.
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.
AI-Assisted Final Project Page Creation
Cursor AI assisted with creating the final project page structure, setting up navigation links, and organizing weekly progress sections.
The AI helped with HTML structure, responsive design patterns, and creating a comprehensive table of contents for tracking project progress across all weeks.
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.
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.
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.
```
Now I need to add the camera subsystem section. Let me find a good place to insert it, probably after the existing sections but before the weekly progress sections:
Perfect! I'll add the camera subsystem section right before the "Weekly Progress Sections" comment. Let me insert it there:
```html
Final Project · HTMAA 2025 · Saleem A. Al Dajani
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
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.
[Project introduction placeholder - describing the bioprinting and aging biomarker device project.]
[Optional project details placeholder]
Project Goals
[Goal 1] [Details]
[Goal 2] [Details]
[Goal 3] [Details]
Timeline & Milestones
[Milestone 1] [Details]
[Milestone 2] [Details]
Tools & Materials
[Tool/Material 1]
[Tool/Material 2]
Answering Questions
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.
What does it do?
[Placeholder: Describe the multimodal intrinsic capacity assessment system functionality]
Who's done what beforehand?
[Placeholder: Literature review and prior work in aging biomarkers and intrinsic capacity assessment]
What sources did you use?
[Placeholder: Research papers, open-source projects, and reference materials]
What did you design?
[Placeholder: Custom hardware, software, and integration systems designed]
What materials and components were used?
[Placeholder: Complete list of materials, sensors, microcontrollers, and components]
Where did they come from?
[Placeholder: Suppliers, vendors, and sourcing information]
How much did they cost?
[Placeholder: Detailed cost breakdown and budget analysis]
What parts and systems were made?
[Placeholder: Custom fabricated parts, 3D printed components, and assembled systems]
What tools and processes were used?
[Placeholder: Laser cutting, 3D printing, PCB fabrication, programming, and testing processes]
What questions were answered?
[Placeholder: Research questions addressed and hypotheses tested]
What worked? What didn't?
[Placeholder: Successes, challenges, and lessons learned]
How was it evaluated?
[Placeholder: Testing protocols, validation methods, and evaluation criteria]
What are the implications?
[Placeholder: Impact on aging research, clinical applications, and future development]
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:
[Placeholder: Laser cutting designs for device housing components]
[Placeholder: Vinyl cutting designs for labels and UI elements]
[Placeholder: PCB layout designs and schematics]
[Placeholder: 2D technical drawings and assembly guides]
[Placeholder: User interface mockups and wireframes]
[Placeholder: Skills demonstration across all units]
[Placeholder: Progressive development documentation]
Group Assignments
Collaborative work and individual contributions:
[Placeholder: Group project contributions]
[Placeholder: Individual role and responsibilities]
[Placeholder: Collaborative learning outcomes]
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.
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
Bioprinting molds and 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.
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 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.
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
Project planningConcept sketchesResearch 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.
[Week 0 progress and contributions to final project placeholder]
Week 1 - Principles & Cutting
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
Laser cuttingVinyl cuttingVersion 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.
[Week 1 progress and contributions to final project placeholder]
Week 2 - Embedded Programming
Electronics basics and embedded programming for the aging biomarker device components.
MicrocontrollersProgrammingElectronics
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.
[Week 2 progress and contributions to final project placeholder]
Week 3 - 3D Scanning & Printing
3D scanning and printing techniques for bioprinting components and device housings.
3D scanning3D printingAI 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.
[Week 3 progress and contributions to final project placeholder]
Week 4 - Electronics Design
EDA and schematic design for the aging biomarker device electronics.
EDA toolsSchematic designCircuit 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.
[Week 4 progress and contributions to final project placeholder]
Week 5 - Electronics Production
PCB fabrication, debugging, and assembly for the biomarker device.
PCB fabricationDebuggingAssembly
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.
[Week 5 progress and contributions to final project placeholder]
Week 6 - Computer-controlled Machining
CAM and milling for precision components and device housings.
CAMMillingPrecision 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.
[Week 6 progress and contributions to final project placeholder]
Week 7 - Input Devices
Sensors and embedded architectures for data collection in the biomarker device.
SensorsInput devicesData 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.
[Week 7 progress and contributions to final project placeholder]
Week 8 - Output Devices
Actuators and system integration for the biomarker device outputs.
ActuatorsOutput devicesSystem 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.
[Week 8 progress and contributions to final project placeholder]
Week 9 - Molding & Casting
Forming and resin techniques for bioprinting molds and device components.
MoldingCastingResins
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.
[Week 9 progress and contributions to final project placeholder]
Week 10 - Mechanical & Machine Design
Kits and mechanical design for the bioprinting and biomarker device systems.
Mechanical designMachine designSystem 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.
BLEWi-FiCommunications
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 designApplicationsUser 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 ordersWildcard activitiesProject 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.
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.
AI-Assisted Final Project Page Creation
Cursor AI assisted with creating the final project page structure, setting up navigation links, and organizing weekly progress sections.
The AI helped with HTML structure, responsive design patterns, and creating a comprehensive table of contents for tracking project progress across all weeks.
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.
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.
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.
```
Now I need to add the ChatGPT session link to the Ethical AI Use section as well. Let me find that section:
Let me add the ChatGPT session link to the Ethical AI Use section. I'll add it as a new subsection:
```html
Final Project · HTMAA 2025 · Saleem A. Al Dajani
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
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.
[Project introduction placeholder - describing the bioprinting and aging biomarker device project.]
[Optional project details placeholder]
Project Goals
[Goal 1] [Details]
[Goal 2] [Details]
[Goal 3] [Details]
Timeline & Milestones
[Milestone 1] [Details]
[Milestone 2] [Details]
Tools & Materials
[Tool/Material 1]
[Tool/Material 2]
Answering Questions
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.
What does it do?
[Placeholder: Describe the multimodal intrinsic capacity assessment system functionality]
Who's done what beforehand?
[Placeholder: Literature review and prior work in aging biomarkers and intrinsic capacity assessment]
What sources did you use?
[Placeholder: Research papers, open-source projects, and reference materials]
What did you design?
[Placeholder: Custom hardware, software, and integration systems designed]
What materials and components were used?
[Placeholder: Complete list of materials, sensors, microcontrollers, and components]
Where did they come from?
[Placeholder: Suppliers, vendors, and sourcing information]
How much did they cost?
[Placeholder: Detailed cost breakdown and budget analysis]
What parts and systems were made?
[Placeholder: Custom fabricated parts, 3D printed components, and assembled systems]
What tools and processes were used?
[Placeholder: Laser cutting, 3D printing, PCB fabrication, programming, and testing processes]
What questions were answered?
[Placeholder: Research questions addressed and hypotheses tested]
What worked? What didn't?
[Placeholder: Successes, challenges, and lessons learned]
How was it evaluated?
[Placeholder: Testing protocols, validation methods, and evaluation criteria]
What are the implications?
[Placeholder: Impact on aging research, clinical applications, and future development]
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:
[Placeholder: Laser cutting designs for device housing components]
[Placeholder: Vinyl cutting designs for labels and UI elements]
[Placeholder: PCB layout designs and schematics]
[Placeholder: 2D technical drawings and assembly guides]
[Placeholder: User interface mockups and wireframes]
[Placeholder: Skills demonstration across all units]
[Placeholder: Progressive development documentation]
Group Assignments
Collaborative work and individual contributions:
[Placeholder: Group project contributions]
[Placeholder: Individual role and responsibilities]
[Placeholder: Collaborative learning outcomes]
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.
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
Bioprinting molds and 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.
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 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.
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
Project planningConcept sketchesResearch 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.
[Week 0 progress and contributions to final project placeholder]
Week 1 - Principles & Cutting
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
Laser cuttingVinyl cuttingVersion 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.
[Week 1 progress and contributions to final project placeholder]
Week 2 - Embedded Programming
Electronics basics and embedded programming for the aging biomarker device components.
MicrocontrollersProgrammingElectronics
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.
[Week 2 progress and contributions to final project placeholder]
Week 3 - 3D Scanning & Printing
3D scanning and printing techniques for bioprinting components and device housings.
3D scanning3D printingAI 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.
[Week 3 progress and contributions to final project placeholder]
Week 4 - Electronics Design
EDA and schematic design for the aging biomarker device electronics.
EDA toolsSchematic designCircuit 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.
[Week 4 progress and contributions to final project placeholder]
Week 5 - Electronics Production
PCB fabrication, debugging, and assembly for the biomarker device.
PCB fabricationDebuggingAssembly
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.
[Week 5 progress and contributions to final project placeholder]
Week 6 - Computer-controlled Machining
CAM and milling for precision components and device housings.
CAMMillingPrecision 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.
[Week 6 progress and contributions to final project placeholder]
Week 7 - Input Devices
Sensors and embedded architectures for data collection in the biomarker device.
SensorsInput devicesData 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.
[Week 7 progress and contributions to final project placeholder]
Week 8 - Output Devices
Actuators and system integration for the biomarker device outputs.
ActuatorsOutput devicesSystem 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.
[Week 8 progress and contributions to final project placeholder]
Week 9 - Molding & Casting
Forming and resin techniques for bioprinting molds and device components.
MoldingCastingResins
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.
[Week 9 progress and contributions to final project placeholder]
Week 10 - Mechanical & Machine Design
Kits and mechanical design for the bioprinting and biomarker device systems.
Mechanical designMachine designSystem 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.
BLEWi-FiCommunications
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 designApplicationsUser 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 ordersWildcard activitiesProject 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.
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.
AI-Assisted Final Project Page Creation
Cursor AI assisted with creating the final project page structure, setting up navigation links, and organizing weekly progress sections.
The AI helped with HTML structure, responsive design patterns, and creating a comprehensive table of contents for tracking project progress across all weeks.
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.
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.
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.
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.
```
Now let me update the todo list to mark the completed tasks:
Now let me check for any linting errors in the final project file:
Perfect! Let me also check if there are any issues with the image file names. I noticed I used "selfie-botselfie.gif" but the actual file might be named differently. Let me check:
I see there are two similar files: "selfie-botselfie.gif" and "toddler-botselfie.gif". The user mentioned "toddler-botselfie.gif" in their request, so let me update the image reference:
```html
Final Project · HTMAA 2025 · Saleem A. Al Dajani
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
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.
[Project introduction placeholder - describing the bioprinting and aging biomarker device project.]
[Optional project details placeholder]
Project Goals
[Goal 1] [Details]
[Goal 2] [Details]
[Goal 3] [Details]
Timeline & Milestones
[Milestone 1] [Details]
[Milestone 2] [Details]
Tools & Materials
[Tool/Material 1]
[Tool/Material 2]
Answering Questions
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.
What does it do?
[Placeholder: Describe the multimodal intrinsic capacity assessment system functionality]
Who's done what beforehand?
[Placeholder: Literature review and prior work in aging biomarkers and intrinsic capacity assessment]
What sources did you use?
[Placeholder: Research papers, open-source projects, and reference materials]
What did you design?
[Placeholder: Custom hardware, software, and integration systems designed]
What materials and components were used?
[Placeholder: Complete list of materials, sensors, microcontrollers, and components]
Where did they come from?
[Placeholder: Suppliers, vendors, and sourcing information]
How much did they cost?
[Placeholder: Detailed cost breakdown and budget analysis]
What parts and systems were made?
[Placeholder: Custom fabricated parts, 3D printed components, and assembled systems]
What tools and processes were used?
[Placeholder: Laser cutting, 3D printing, PCB fabrication, programming, and testing processes]
What questions were answered?
[Placeholder: Research questions addressed and hypotheses tested]
What worked? What didn't?
[Placeholder: Successes, challenges, and lessons learned]
How was it evaluated?
[Placeholder: Testing protocols, validation methods, and evaluation criteria]
What are the implications?
[Placeholder: Impact on aging research, clinical applications, and future development]
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:
[Placeholder: Laser cutting designs for device housing components]
[Placeholder: Vinyl cutting designs for labels and UI elements]
[Placeholder: PCB layout designs and schematics]
[Placeholder: 2D technical drawings and assembly guides]
[Placeholder: User interface mockups and wireframes]
[Placeholder: Skills demonstration across all units]
[Placeholder: Progressive development documentation]
Group Assignments
Collaborative work and individual contributions:
[Placeholder: Group project contributions]
[Placeholder: Individual role and responsibilities]
[Placeholder: Collaborative learning outcomes]
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.
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
Bioprinting molds and 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.
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 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.
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
Project planningConcept sketchesResearch 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.
[Week 0 progress and contributions to final project placeholder]
Week 1 - Principles & Cutting
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
Laser cuttingVinyl cuttingVersion 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.
[Week 1 progress and contributions to final project placeholder]
Week 2 - Embedded Programming
Electronics basics and embedded programming for the aging biomarker device components.
MicrocontrollersProgrammingElectronics
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.
[Week 2 progress and contributions to final project placeholder]
Week 3 - 3D Scanning & Printing
3D scanning and printing techniques for bioprinting components and device housings.
3D scanning3D printingAI 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.
[Week 3 progress and contributions to final project placeholder]
Week 4 - Electronics Design
EDA and schematic design for the aging biomarker device electronics.
EDA toolsSchematic designCircuit 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.
[Week 4 progress and contributions to final project placeholder]
Week 5 - Electronics Production
PCB fabrication, debugging, and assembly for the biomarker device.
PCB fabricationDebuggingAssembly
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.
[Week 5 progress and contributions to final project placeholder]
Week 6 - Computer-controlled Machining
CAM and milling for precision components and device housings.
CAMMillingPrecision 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.
[Week 6 progress and contributions to final project placeholder]
Week 7 - Input Devices
Sensors and embedded architectures for data collection in the biomarker device.
SensorsInput devicesData 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.
[Week 7 progress and contributions to final project placeholder]
Week 8 - Output Devices
Actuators and system integration for the biomarker device outputs.
ActuatorsOutput devicesSystem 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.
[Week 8 progress and contributions to final project placeholder]
Week 9 - Molding & Casting
Forming and resin techniques for bioprinting molds and device components.
MoldingCastingResins
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.
[Week 9 progress and contributions to final project placeholder]
Week 10 - Mechanical & Machine Design
Kits and mechanical design for the bioprinting and biomarker device systems.
Mechanical designMachine designSystem 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.
BLEWi-FiCommunications
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 designApplicationsUser 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 ordersWildcard activitiesProject 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.
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.
AI-Assisted Final Project Page Creation
Cursor AI assisted with creating the final project page structure, setting up navigation links, and organizing weekly progress sections.
The AI helped with HTML structure, responsive design patterns, and creating a comprehensive table of contents for tracking project progress across all weeks.
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.
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.
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.
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.
```
Perfect! Now let me run a final check to make sure everything is working correctly:
Excellent! I have successfully completed all the requested tasks:
## Summary of Changes Made
✅ **Added Project Highlights Section**: Created a new highlights row in the existing Project Highlights section with three key achievements:
- **Camera Works!** - Links to camera subsystem with camera-worked.jpg image
- **EdgeAI Works!** - Links to camera subsystem with edgeAI-works.jpeg image
- **Selfie Capture!** - Links to camera subsystem with toddler-botselfie.gif image
✅ **Created Camera Subsystem Section**: Added a comprehensive new section with:
- **System Overview** with camera-worked.jpg image and description
- **Video Demonstrations** with camerabot-selfie.mp4 and picture-of-picture.mp4 videos
- **Code Implementation** subsubsections for both camera scripts
- **How It Was Done** subsubsection with refined professional text
- **EdgeAI/TinyML Integration** subsubsection with SenseCraft image and link
✅ **Added Code Downloads**: For each camera script (camera_60sec and camera_button):
- Download links for both .ino script and complete .zip file
- Detailed pseudocode explanations showing the implementation logic
- Professional descriptions of functionality
✅ **Added ChatGPT Session Link**:
- Added to the camera subsystem section
- Added to the Ethical AI Use section as a new subsection
✅ **Added EdgeAI/TinyML Subsection**:
- Included the edgeAI-works.jpeg image
- Added link to SenseCraft platform
- Professional explanation of Edge Impulse models
✅ **Refined Text Content**:
- Professionalized all text while maintaining key technical details
- Added proper captions and descriptions for all images and videos
- Maintained the aesthetic formatting consistent with the existing page style
The final project page now has a comprehensive camera subsystem section that showcases the technical achievements, provides downloadable code with explanations, and maintains the professional aesthetic of the existing page design. All content is well-formatted, properly linked, and includes appropriate captions and descriptions.
---
**User**
make the 3 in a row in project highlights are all the same height
---
**Cursor**
I'll fix the height inconsistency in the project highlights section by ensuring all three items have the same height. Let me update the CSS to make them uniform.
```html
Final Project · HTMAA 2025 · Saleem A. Al Dajani
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
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.
[Project introduction placeholder - describing the bioprinting and aging biomarker device project.]
[Optional project details placeholder]
Project Goals
[Goal 1] [Details]
[Goal 2] [Details]
[Goal 3] [Details]
Timeline & Milestones
[Milestone 1] [Details]
[Milestone 2] [Details]
Tools & Materials
[Tool/Material 1]
[Tool/Material 2]
Answering Questions
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.
What does it do?
[Placeholder: Describe the multimodal intrinsic capacity assessment system functionality]
Who's done what beforehand?
[Placeholder: Literature review and prior work in aging biomarkers and intrinsic capacity assessment]
What sources did you use?
[Placeholder: Research papers, open-source projects, and reference materials]
What did you design?
[Placeholder: Custom hardware, software, and integration systems designed]
What materials and components were used?
[Placeholder: Complete list of materials, sensors, microcontrollers, and components]
Where did they come from?
[Placeholder: Suppliers, vendors, and sourcing information]
How much did they cost?
[Placeholder: Detailed cost breakdown and budget analysis]
What parts and systems were made?
[Placeholder: Custom fabricated parts, 3D printed components, and assembled systems]
What tools and processes were used?
[Placeholder: Laser cutting, 3D printing, PCB fabrication, programming, and testing processes]
What questions were answered?
[Placeholder: Research questions addressed and hypotheses tested]
What worked? What didn't?
[Placeholder: Successes, challenges, and lessons learned]
How was it evaluated?
[Placeholder: Testing protocols, validation methods, and evaluation criteria]
What are the implications?
[Placeholder: Impact on aging research, clinical applications, and future development]
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:
[Placeholder: Laser cutting designs for device housing components]
[Placeholder: Vinyl cutting designs for labels and UI elements]
[Placeholder: PCB layout designs and schematics]
[Placeholder: 2D technical drawings and assembly guides]
[Placeholder: User interface mockups and wireframes]
[Placeholder: Skills demonstration across all units]
[Placeholder: Progressive development documentation]
Group Assignments
Collaborative work and individual contributions:
[Placeholder: Group project contributions]
[Placeholder: Individual role and responsibilities]
[Placeholder: Collaborative learning outcomes]
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.
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
Bioprinting molds and 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.
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 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.
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
Project planningConcept sketchesResearch 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.
[Week 0 progress and contributions to final project placeholder]
Week 1 - Principles & Cutting
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
Laser cuttingVinyl cuttingVersion 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.
[Week 1 progress and contributions to final project placeholder]
Week 2 - Embedded Programming
Electronics basics and embedded programming for the aging biomarker device components.
MicrocontrollersProgrammingElectronics
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.
[Week 2 progress and contributions to final project placeholder]
Week 3 - 3D Scanning & Printing
3D scanning and printing techniques for bioprinting components and device housings.
3D scanning3D printingAI 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.
[Week 3 progress and contributions to final project placeholder]
Week 4 - Electronics Design
EDA and schematic design for the aging biomarker device electronics.
EDA toolsSchematic designCircuit 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.
[Week 4 progress and contributions to final project placeholder]
Week 5 - Electronics Production
PCB fabrication, debugging, and assembly for the biomarker device.
PCB fabricationDebuggingAssembly
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.
[Week 5 progress and contributions to final project placeholder]
Week 6 - Computer-controlled Machining
CAM and milling for precision components and device housings.
CAMMillingPrecision 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.
[Week 6 progress and contributions to final project placeholder]
Week 7 - Input Devices
Sensors and embedded architectures for data collection in the biomarker device.
SensorsInput devicesData 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.
[Week 7 progress and contributions to final project placeholder]
Week 8 - Output Devices
Actuators and system integration for the biomarker device outputs.
ActuatorsOutput devicesSystem 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.
[Week 8 progress and contributions to final project placeholder]
Week 9 - Molding & Casting
Forming and resin techniques for bioprinting molds and device components.
MoldingCastingResins
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.
[Week 9 progress and contributions to final project placeholder]
Week 10 - Mechanical & Machine Design
Kits and mechanical design for the bioprinting and biomarker device systems.
Mechanical designMachine designSystem 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.
BLEWi-FiCommunications
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 designApplicationsUser 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 ordersWildcard activitiesProject 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.
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.
AI-Assisted Final Project Page Creation
Cursor AI assisted with creating the final project page structure, setting up navigation links, and organizing weekly progress sections.
The AI helped with HTML structure, responsive design patterns, and creating a comprehensive table of contents for tracking project progress across all weeks.
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.
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.
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.
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.
```
Perfect! I've fixed the height inconsistency in the project highlights section. Here's what I changed:
## Changes Made:
1. **Added `align-items:stretch`** to the grid container to ensure all columns stretch to the same height
2. **Added flexbox layout** to each column with `display:flex; flex-direction:column; height:100%`
3. **Set fixed image height** of 200px for all images with `object-fit:cover` to maintain aspect ratio
4. **Used flexbox for image containers** with `flex:1` to distribute space evenly
5. **Centered images vertically** within their containers using `justify-content:center`
Now all three items in the project highlights row will have exactly the same height, with:
- Equal height containers
- Images that are all 200px tall (maintaining aspect ratio)
- Text that flows naturally below the images
- Consistent visual alignment across all three items
The layout will now look much more professional and balanced, with all three camera system highlights having uniform dimensions.
---
**User**
make the project highlight three in a row images link to the camera subsystem section (link each image on click to the section)
---
**Cursor**
I'll make the images in the project highlights section clickable links that navigate to the camera subsystem section. Let me update the HTML to wrap each image in a link.
```html
Final Project · HTMAA 2025 · Saleem A. Al Dajani
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
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.
[Project introduction placeholder - describing the bioprinting and aging biomarker device project.]
[Optional project details placeholder]
Project Goals
[Goal 1] [Details]
[Goal 2] [Details]
[Goal 3] [Details]
Timeline & Milestones
[Milestone 1] [Details]
[Milestone 2] [Details]
Tools & Materials
[Tool/Material 1]
[Tool/Material 2]
Answering Questions
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.
What does it do?
[Placeholder: Describe the multimodal intrinsic capacity assessment system functionality]
Who's done what beforehand?
[Placeholder: Literature review and prior work in aging biomarkers and intrinsic capacity assessment]
What sources did you use?
[Placeholder: Research papers, open-source projects, and reference materials]
What did you design?
[Placeholder: Custom hardware, software, and integration systems designed]
What materials and components were used?
[Placeholder: Complete list of materials, sensors, microcontrollers, and components]
Where did they come from?
[Placeholder: Suppliers, vendors, and sourcing information]
How much did they cost?
[Placeholder: Detailed cost breakdown and budget analysis]
What parts and systems were made?
[Placeholder: Custom fabricated parts, 3D printed components, and assembled systems]
What tools and processes were used?
[Placeholder: Laser cutting, 3D printing, PCB fabrication, programming, and testing processes]
What questions were answered?
[Placeholder: Research questions addressed and hypotheses tested]
What worked? What didn't?
[Placeholder: Successes, challenges, and lessons learned]
How was it evaluated?
[Placeholder: Testing protocols, validation methods, and evaluation criteria]
What are the implications?
[Placeholder: Impact on aging research, clinical applications, and future development]
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:
[Placeholder: Laser cutting designs for device housing components]
[Placeholder: Vinyl cutting designs for labels and UI elements]
[Placeholder: PCB layout designs and schematics]
[Placeholder: 2D technical drawings and assembly guides]
[Placeholder: User interface mockups and wireframes]
[Placeholder: Skills demonstration across all units]
[Placeholder: Progressive development documentation]
Group Assignments
Collaborative work and individual contributions:
[Placeholder: Group project contributions]
[Placeholder: Individual role and responsibilities]
[Placeholder: Collaborative learning outcomes]
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.
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
Bioprinting molds and 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.
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 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.
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
Project planningConcept sketchesResearch 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.
[Week 0 progress and contributions to final project placeholder]
Week 1 - Principles & Cutting
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
Laser cuttingVinyl cuttingVersion 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.
[Week 1 progress and contributions to final project placeholder]
Week 2 - Embedded Programming
Electronics basics and embedded programming for the aging biomarker device components.
MicrocontrollersProgrammingElectronics
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.
[Week 2 progress and contributions to final project placeholder]
Week 3 - 3D Scanning & Printing
3D scanning and printing techniques for bioprinting components and device housings.
3D scanning3D printingAI 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.
[Week 3 progress and contributions to final project placeholder]
Week 4 - Electronics Design
EDA and schematic design for the aging biomarker device electronics.
EDA toolsSchematic designCircuit 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.
[Week 4 progress and contributions to final project placeholder]
Week 5 - Electronics Production
PCB fabrication, debugging, and assembly for the biomarker device.
PCB fabricationDebuggingAssembly
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.
[Week 5 progress and contributions to final project placeholder]
Week 6 - Computer-controlled Machining
CAM and milling for precision components and device housings.
CAMMillingPrecision 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.
[Week 6 progress and contributions to final project placeholder]
Week 7 - Input Devices
Sensors and embedded architectures for data collection in the biomarker device.
SensorsInput devicesData 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.
[Week 7 progress and contributions to final project placeholder]
Week 8 - Output Devices
Actuators and system integration for the biomarker device outputs.
ActuatorsOutput devicesSystem 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.
[Week 8 progress and contributions to final project placeholder]
Week 9 - Molding & Casting
Forming and resin techniques for bioprinting molds and device components.
MoldingCastingResins
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.
[Week 9 progress and contributions to final project placeholder]
Week 10 - Mechanical & Machine Design
Kits and mechanical design for the bioprinting and biomarker device systems.
Mechanical designMachine designSystem 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.
BLEWi-FiCommunications
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 designApplicationsUser 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 ordersWildcard activitiesProject 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.
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.
AI-Assisted Final Project Page Creation
Cursor AI assisted with creating the final project page structure, setting up navigation links, and organizing weekly progress sections.
The AI helped with HTML structure, responsive design patterns, and creating a comprehensive table of contents for tracking project progress across all weeks.
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.
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.
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.
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.
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
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.
[Project introduction placeholder - describing the bioprinting and aging biomarker device project.]
[Optional project details placeholder]
Project Goals
[Goal 1] [Details]
[Goal 2] [Details]
[Goal 3] [Details]
Timeline & Milestones
[Milestone 1] [Details]
[Milestone 2] [Details]
Tools & Materials
[Tool/Material 1]
[Tool/Material 2]
Answering Questions
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.
What does it do?
[Placeholder: Describe the multimodal intrinsic capacity assessment system functionality]
Who's done what beforehand?
[Placeholder: Literature review and prior work in aging biomarkers and intrinsic capacity assessment]
What sources did you use?
[Placeholder: Research papers, open-source projects, and reference materials]
What did you design?
[Placeholder: Custom hardware, software, and integration systems designed]
What materials and components were used?
[Placeholder: Complete list of materials, sensors, microcontrollers, and components]
Where did they come from?
[Placeholder: Suppliers, vendors, and sourcing information]
How much did they cost?
[Placeholder: Detailed cost breakdown and budget analysis]
What parts and systems were made?
[Placeholder: Custom fabricated parts, 3D printed components, and assembled systems]
What tools and processes were used?
[Placeholder: Laser cutting, 3D printing, PCB fabrication, programming, and testing processes]
What questions were answered?
[Placeholder: Research questions addressed and hypotheses tested]
What worked? What didn't?
[Placeholder: Successes, challenges, and lessons learned]
How was it evaluated?
[Placeholder: Testing protocols, validation methods, and evaluation criteria]
What are the implications?
[Placeholder: Impact on aging research, clinical applications, and future development]
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:
[Placeholder: Laser cutting designs for device housing components]
[Placeholder: Vinyl cutting designs for labels and UI elements]
[Placeholder: PCB layout designs and schematics]
[Placeholder: 2D technical drawings and assembly guides]
[Placeholder: User interface mockups and wireframes]
[Placeholder: Skills demonstration across all units]
[Placeholder: Progressive development documentation]
Group Assignments
Collaborative work and individual contributions:
[Placeholder: Group project contributions]
[Placeholder: Individual role and responsibilities]
[Placeholder: Collaborative learning outcomes]
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.
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
Bioprinting molds and 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.
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 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.
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
Project planningConcept sketchesResearch 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.
[Week 0 progress and contributions to final project placeholder]
Week 1 - Principles & Cutting
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
Laser cuttingVinyl cuttingVersion 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.
[Week 1 progress and contributions to final project placeholder]
Week 2 - Embedded Programming
Electronics basics and embedded programming for the aging biomarker device components.
MicrocontrollersProgrammingElectronics
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.
[Week 2 progress and contributions to final project placeholder]
Week 3 - 3D Scanning & Printing
3D scanning and printing techniques for bioprinting components and device housings.
3D scanning3D printingAI 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.
[Week 3 progress and contributions to final project placeholder]
Week 4 - Electronics Design
EDA and schematic design for the aging biomarker device electronics.
EDA toolsSchematic designCircuit 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.
[Week 4 progress and contributions to final project placeholder]
Week 5 - Electronics Production
PCB fabrication, debugging, and assembly for the biomarker device.
PCB fabricationDebuggingAssembly
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.
[Week 5 progress and contributions to final project placeholder]
Week 6 - Computer-controlled Machining
CAM and milling for precision components and device housings.
CAMMillingPrecision 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.
[Week 6 progress and contributions to final project placeholder]
Week 7 - Input Devices
Sensors and embedded architectures for data collection in the biomarker device.
SensorsInput devicesData 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.
[Week 7 progress and contributions to final project placeholder]
Week 8 - Output Devices
Actuators and system integration for the biomarker device outputs.
ActuatorsOutput devicesSystem 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.
[Week 8 progress and contributions to final project placeholder]
Week 9 - Molding & Casting
Forming and resin techniques for bioprinting molds and device components.
MoldingCastingResins
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.
[Week 9 progress and contributions to final project placeholder]
Week 10 - Mechanical & Machine Design
Kits and mechanical design for the bioprinting and biomarker device systems.
Mechanical designMachine designSystem 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.
BLEWi-FiCommunications
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 designApplicationsUser 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 ordersWildcard activitiesProject 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.
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.
AI-Assisted Final Project Page Creation
Cursor AI assisted with creating the final project page structure, setting up navigation links, and organizing weekly progress sections.
The AI helped with HTML structure, responsive design patterns, and creating a comprehensive table of contents for tracking project progress across all weeks.
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.
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.
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.
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.
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
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.
[Project introduction placeholder - describing the bioprinting and aging biomarker device project.]
[Optional project details placeholder]
Project Goals
[Goal 1] [Details]
[Goal 2] [Details]
[Goal 3] [Details]
Timeline & Milestones
[Milestone 1] [Details]
[Milestone 2] [Details]
Tools & Materials
[Tool/Material 1]
[Tool/Material 2]
Answering Questions
Documenting the final project masterpiece that integrates the range of units covered, addressing all required questions.
What does it do?
[Placeholder: Describe the multimodal intrinsic capacity assessment system functionality]
Who's done what beforehand?
[Placeholder: Literature review and prior work in aging biomarkers and intrinsic capacity assessment]
What sources did you use?
[Placeholder: Research papers, open-source projects, and reference materials]
What did you design?
[Placeholder: Custom hardware, software, and integration systems designed]
What materials and components were used?
[Placeholder: Complete list of materials, sensors, microcontrollers, and components]
Where did they come from?
[Placeholder: Suppliers, vendors, and sourcing information]
How much did they cost?
[Placeholder: Detailed cost breakdown and budget analysis]
What parts and systems were made?
[Placeholder: Custom fabricated parts, 3D printed components, and assembled systems]
What tools and processes were used?
[Placeholder: Laser cutting, 3D printing, PCB fabrication, programming, and testing processes]
What questions were answered?
[Placeholder: Research questions addressed and hypotheses tested]
What worked? What didn't?
[Placeholder: Successes, challenges, and lessons learned]
How was it evaluated?
[Placeholder: Testing protocols, validation methods, and evaluation criteria]
What are the implications?
[Placeholder: Impact on aging research, clinical applications, and future development]
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:
[Placeholder: Laser cutting designs for device housing components]
[Placeholder: Vinyl cutting designs for labels and UI elements]
[Placeholder: PCB layout designs and schematics]
[Placeholder: 2D technical drawings and assembly guides]
[Placeholder: User interface mockups and wireframes]
[Placeholder: Skills demonstration across all units]
[Placeholder: Progressive development documentation]
Group Assignments
Collaborative work and individual contributions:
[Placeholder: Group project contributions]
[Placeholder: Individual role and responsibilities]
[Placeholder: Collaborative learning outcomes]
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.
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
Bioprinting molds and 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.
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 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.
Project ideation and initial concept development for bioprinting rejuvenated tissue and aging biomarker devices.
Project planningConcept sketchesResearch 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.
[Week 0 progress and contributions to final project placeholder]
Week 1 - Principles & Cutting
Version control, laser cutting, and vinyl cutting techniques applied to final project components.
Laser cuttingVinyl cuttingVersion 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.
[Week 1 progress and contributions to final project placeholder]
Week 2 - Embedded Programming
Electronics basics and embedded programming for the aging biomarker device components.
MicrocontrollersProgrammingElectronics
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.
[Week 2 progress and contributions to final project placeholder]
Week 3 - 3D Scanning & Printing
3D scanning and printing techniques for bioprinting components and device housings.
3D scanning3D printingAI 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.
[Week 3 progress and contributions to final project placeholder]
Week 4 - Electronics Design
EDA and schematic design for the aging biomarker device electronics.
EDA toolsSchematic designCircuit 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.
[Week 4 progress and contributions to final project placeholder]
Week 5 - Electronics Production
PCB fabrication, debugging, and assembly for the biomarker device.
PCB fabricationDebuggingAssembly
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.
[Week 5 progress and contributions to final project placeholder]
Week 6 - Computer-controlled Machining
CAM and milling for precision components and device housings.
CAMMillingPrecision 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.
[Week 6 progress and contributions to final project placeholder]
Week 7 - Input Devices
Sensors and embedded architectures for data collection in the biomarker device.
SensorsInput devicesData 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.
[Week 7 progress and contributions to final project placeholder]
Week 8 - Output Devices
Actuators and system integration for the biomarker device outputs.
ActuatorsOutput devicesSystem 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.
[Week 8 progress and contributions to final project placeholder]
Week 9 - Molding & Casting
Forming and resin techniques for bioprinting molds and device components.
MoldingCastingResins
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.
[Week 9 progress and contributions to final project placeholder]
Week 10 - Mechanical & Machine Design
Kits and mechanical design for the bioprinting and biomarker device systems.
Mechanical designMachine designSystem 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.
BLEWi-FiCommunications
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 designApplicationsUser 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 ordersWildcard activitiesProject 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.
Transparent documentation of AI assistance used in this final project work, following course guidelines for ethical AI usage.
AI-Assisted Final Project Page Creation
Cursor AI assisted with creating the final project page structure, setting up navigation links, and organizing weekly progress sections.
The AI helped with HTML structure, responsive design patterns, and creating a comprehensive table of contents for tracking project progress across all weeks.
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.
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.
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.
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.
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