Author: revap

Progress Update:

This week, I fine-tuned the data processing algorithms, focusing on enhancing noise reduction and optimizing pitch-based thresholding for footstrike detection. The refined algorithms were integrated into the main application and tested using simulated datasets. These tests demonstrated that metrics like footstep detection are functioning well overall, but using this data to calculate stride length has proven more challenging due to variations in user gait and step patterns. The app’s existing visualization was instrumental in verifying real-time data flow and detection accuracy.

Challenges Faced:

1. Stride Length Calculation: Developing a reliable method to compute stride length from footstep detection data, accounting for individual variations in stride dynamics.
2. Real-Time Integration: Ensuring the processing algorithms operate efficiently within the app without impacting performance.
3. Edge Cases: Simulated datasets highlighted specific scenarios, such as abrupt movement changes, that require further refinement in the detection algorithms.

Next Steps:

1. Test the app with live data collected from real-world runs to validate the algorithms under dynamic conditions and gather insights for stride length calculation.
2. Refine the detection algorithms to improve robustness in handling edge cases and variability in user gaits.
3. Seek feedback from initial users to identify pain points and guide further optimization of both detection and stride length computation.

Progress Update:

This week, I focused on implementing smoothing algorithms and moving averages to process the incoming data. These techniques help in reducing noise and making the data more suitable for analysis. I also worked on a thresholding algorithm that uses the pitch of the device as a means of detecting footstrikes. By analyzing the changes in pitch, we can more accurately determine when a footstrike occurs.

Challenges Faced:

• Algorithm Optimization: Ensuring the smoothing and moving average algorithms do not distort the essential features of the data needed for accurate footstrike detection.
• Threshold Calibration: Determining the optimal threshold values for pitch changes to reliably detect footstrikes without false positives.

Next Steps:

• Validate the smoothing and thresholding algorithms with a larger dataset.
• Integrate the footstrike detection feature into the main application.
• Collect feedback from initial tests to further refine the algorithms.

Progress Update

1. Buffer System and Data Handling:
   • Implemented a buffer system to manage incoming data, which is flushed into a local data structure in Dart for real-time processing.
   • Buffered data is being saved into a CSV file for further analysis and visualization.
   • Began developing algorithms to parse through the data structure and CSV file to detect footstrikes, a critical metric for the project.
2. Integration of New Hardware:
   • Received the new Yost sensor and started preparation for integration into the existing system.
   • The Bosch IMU has been successfully configured to communicate motion data to the ESP32, allowing for accurate data capture and processing.
3. Data Logging and Transfer:
   • Set up a pipeline to log motion data onto an SD card for structured analysis.
   • Implemented BLE functionality to transfer the data as a .txt file from the SD card directly to the mobile app, enhancing accessibility and user interaction.

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Challenges Faced

1. Data Visualization:
   • Developing intuitive and informative visual representations of buffered and logged data remains a challenge.
2. Footstrike Detection Algorithm:
   • Parsing complex datasets to accurately detect footstrikes requires fine-tuning and validation against real-world motion scenarios.
3. Hardware Integration:
   • The soldering and handling of the Yost sensor require precision to ensure stable connectivity and functionality.

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Key Achievements

• Data Capture: Successfully configured the Bosch IMU for seamless communication with the ESP32.
• Data Logging: Created a robust pipeline for logging motion data onto an SD card.
• BLE Transfer: Enabled BLE functionality to transfer .txt files directly to the app, improving user interaction.
• Buffer System: Established a reliable buffer system for efficient data management and storage.

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Next Steps

1. Hardware Integration:
   • Proceed with soldering the Yost sensor and test its compatibility with the current system.
2. Footstrike Detection:
   • Continue developing and validating the footstrike detection algorithm using collected data.
3. Data Visualization:
   • Refine visualization tools within the app to better represent trends and patterns in the motion data.
4. Performance Testing:
   • Test system performance under various conditions to ensure stable data transfer and app connectivity via BLE.
5. Optimization:
   • Enhance the buffer system for improved performance and reliability.

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Conclusion

The project is progressing well, with significant milestones achieved in data capture, logging, and BLE transfer. Integration of the new Yost sensor will expand the system’s capabilities, while ongoing improvements in data visualization and footstrike detection algorithms will provide actionable insights. The team remains focused on refining the system for enhanced accuracy, reliability, and user experience.

Progress Update:

We have implemented a buffer system to flush incoming data into a local data structure in Dart. This buffered data is also being saved into a CSV file for further analysis. We are currently working on visualizing this data to better understand the patterns and trends. Additionally, we are developing algorithms to parse through the data structure and the CSV file to detect footstrikes, which is a critical component of our project.

Challenges Faced:

• Data Visualization: Creating intuitive and informative visual representations of the buffered data.
• Footstrike Detection Algorithm: Parsing complex data to accurately detect footstrikes requires fine-tuning and validation against real-world scenarios.

Next Steps:

• Continue refining the data visualization tools within the app.
• Test and improve the footstrike detection algorithm using the collected data.
• Optimize the buffer system for better performance and reliability.

Progress Update

This week, the team made significant progress on multiple fronts:

1. Bluetooth Connection:
   • Successfully established a robust Bluetooth connection between the ESP32 device and the mobile application.
   • Developed a protocol to send structured data (using structs) to the app, improving the efficiency and reliability of data transmission.
   • Implemented a transfer status ID to indicate the end of data transmission, ensuring the app can detect when a complete data packet has been received.
2. Data Acquisition and Processing:
   • Acquired raw linear acceleration data along the X-axis from the BNO055 IMU in NDOF mode.
   • Applied exponential smoothing to reduce high-frequency noise, stabilizing the acceleration signal for further analysis.
   • Corrected sensor bias by recording a stationary baseline and subtracting the offset from subsequent readings.
   • Calculated velocity (through first integration) and displacement (through second integration) based on the smoothed acceleration data, using a fixed time interval determined by the IMU’s sampling rate.
3. Data Transmission:
   • Configured the ESP32 as a BLE server for transmitting processed IMU data to the app.
   • Formatted the data into strings for compatibility with the app’s parsing requirements.
4. UI Improvements:
   • Added a user interface section to input height and weight, which will be used in future analytics.
   • Enhanced the visualization of data, focusing on graphs and trends to provide more intuitive insights for users.

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Challenges Faced

1. Data Transmission Timing Issues:
   • The asynchronous nature of Bluetooth communication led to timing discrepancies, with data packets occasionally being received out of order.
2. Noise and Drift in Displacement Calculation:
   • Residual noise in acceleration data caused drift in velocity and displacement estimates, even after smoothing.
3. Temperature and Environmental Sensitivity:
   • Minor variations in IMU data were observed under different temperature conditions, highlighting the need for temperature-based calibration.
4. ESP32 Power Management:
   • To prevent overheating and optimize power usage, delays were implemented between transmissions. Additionally, Wi-Fi settings were optimized to minimize power consumption during idle periods.

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Results

1. Processed Data:
   • Successfully transmitted smoothed acceleration, velocity, and displacement data from the ESP32 to the app.
   • Users can now monitor these values through the mobile app.
2. Key Outputs:
   • Velocity (X-axis): Estimated velocity over time.
   • Displacement (X-axis): Estimated displacement via double integration of acceleration data.

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Next Steps

1. Data Transmission Improvements:
   • Investigate and resolve timing issues to ensure data packets are received in the correct order.
   • Implement error-checking mechanisms to handle unordered data reception.
   • Conduct extensive testing under different conditions to identify and mitigate bottlenecks.
2. Advanced Filtering:
   • Explore the use of a Kalman filter to reduce drift and improve accuracy in displacement and velocity calculations.
3. App Enhancements:
   • Add data visualization features, such as graphs and trendlines, to improve user insights into acceleration, velocity, and displacement trends.
4. Temperature Calibration:
   • Investigate temperature sensitivity of the IMU and incorporate calibration techniques to minimize its impact.

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Conclusion

The team has made significant progress in both hardware communication and data processing. The establishment of Bluetooth communication and successful implementation of data acquisition and smoothing techniques lay the groundwork for further improvements. Challenges such as timing discrepancies, noise-induced drift, and environmental sensitivities have been identified and are being addressed. Upcoming efforts will focus on advanced filtering techniques, enhanced data visualization, and continued system optimization to ensure reliability and usability for end-users.

Progress Update:

This week, we successfully solidified the Bluetooth connection between our ESP32 device and the mobile application. We developed a protocol to send structured data (using structs) to the app, which has improved the efficiency and reliability of our data transmission. Additionally, we implemented a transfer status ID to indicate the end of data transmission, ensuring that the app knows when a data packet is fully received.

Challenges Faced:

• Timing Issues: We are experiencing problems with data not being received in order. The asynchronous nature of Bluetooth communication is causing timing discrepancies, leading to out-of-order data packets.

Next Steps:

• Investigate and resolve the timing issues to ensure data packets are received in the correct order.
• Implement error-checking mechanisms to handle any unordered data reception.
• Begin initial testing of data transmission under different conditions to identify potential bottlenecks.