Team Status Report for 11/30

Team Status Report

Testing Summary (Vansh)

Participants:

• Height: 6 feet, Gender: Male
• Height: 5 feet 8 inches, Gender: Male
• Height: 5 feet, Gender: Female

Key Objectives:

1. Evaluate ease of use and comfort during wear.
2. Assess user feedback on the insole’s feel.
3. Validate step-count accuracy.
4. Identify and fix any firmware-related issues.

Findings:

• Usability & Comfort:
  The device was easy to use, with participants adapting quickly. Feedback on comfort was positive, with slight material adjustments suggested for the shortest participant.
• Step-Count Refinement:
  Improved calibration of the step-detection algorithm ensured accurate results for all participants, accommodating differences in stride length and height.
• Firmware Updates:
  Resolved a critical BLE transmission bug by ensuring the data buffer is cleared onto the SD card before Bluetooth transmission. This improved reliability and data integrity.

Additional Progress:

• Second Shoe Prototype:
  Fabricated the second insole prototype to enable bilateral motion analysis.
• Final Presentation:
  Refined the project’s presentation, integrating visuals and data to showcase testing results and milestones.

New Tools and Knowledge Acquisition:

• Firmware Development: Explored SD card management and BLE protocols.
• Hardware Integration: Worked with ESP32 and Bosch IMU to minimize sensor drift.
• MATLAB Simulations: Conducted simulations to optimize system parameters.

Learning Strategies: Utilized YouTube tutorials, GitHub repositories, and forums like Stack Overflow for quick learning and problem-solving.

Next Steps:

• Finalize the second shoe prototype for bilateral testing.
• Expand participant testing.
• Refine the final presentation and incorporate additional data visualizations.

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Progress Update (Reva)

Progress:

• Refined and implemented data processing algorithms, including smoothing, moving averages, and pitch-based thresholding for footstrike detection.
• Initial testing within the development environment highlighted areas for improvement in noise reduction and threshold calibration.

Challenges:

• Noise Filtering: Balancing smoothing algorithms to reduce noise without compromising footstrike detection accuracy.
• Threshold Calibration: Determining pitch threshold values for reliable footstrike identification across diverse running styles.
• Integration: Ensuring efficient algorithm performance within the development environment without lag.

Next Steps:

1. Fine-tune data processing algorithms to enhance accuracy and reliability.
2. Test the system under varied gait patterns and terrains.
3. Integrate refined algorithms into the main application for further testing and visualization.

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Combined Next Steps:

1. Finalize the second shoe prototype and conduct bilateral testing.
2. Complete fine-tuning of data processing algorithms to handle diverse gait patterns effectively.
3. Conduct additional participant testing under varied conditions to optimize step detection and stride length calculations.
4. Refine and test the firmware for consistent real-world performance.
5. Finalize the project’s presentation, integrating additional data and insights from expanded testing.