Category: Uncategorized

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.

Summary of Work Done

1. Data Acquisition and Smoothing
   • Raw linear acceleration data along the X-axis was acquired from the BNO055 IMU in NDOF mode.
   • Initial analysis showed significant noise in the raw data even when the sensor was stationary. To address this, we applied exponential smoothing to reduce high-frequency noise and stabilize the signal for further processing. This step was essential to obtain a more accurate representation of linear acceleration.
2. Bias Correction
   • A bias correction technique was implemented by recording a stationary baseline and calculating the mean of the initial readings to account for any offset. This average offset was then subtracted from all subsequent readings, ensuring that the data approached zero when the IMU was stationary.
3. Velocity and Displacement Calculation
   • First Integration: The smoothed and bias-corrected acceleration data was integrated over time to estimate velocity.
   • Second Integration: The calculated velocity was further integrated over time to estimate displacement along the X-axis.
   • A fixed time interval (dt), based on the IMU’s sampling rate, was used to ensure consistent and accurate integration at each step.
4. ESP32 Data Transmission to Mobile App
   • We used an ESP32 microcontroller to transmit the processed IMU data to a mobile app. Here’s how we set up this data transmission:
     • BLE Communication: The ESP32 was configured to create a BLE server, allowing it to communicate wirelessly with the mobile app.
     • Data Formatting: The processed data (smoothed acceleration, velocity, and displacement) was formatted into strings to ensure compatibility with the app’s data parsing requirements.
5. Challenges Encountered in Transmission
   • Power Management on ESP32: To prevent overheating and optimize power usage, we implemented a delay between data transmissions. Additionally, we configured the ESP32’s Wi-Fi settings to minimize power consumption during idle periods.

Results

The processed data was successfully transmitted from the ESP32 to the mobile app, allowing users to monitor acceleration, velocity, and displacement values. Key outputs included:

• Velocity (X-axis): An estimated velocity value over time.
• Displacement (X-axis): An estimated displacement value based on double integration of acceleration data.

Challenges and Recommendations

1. Noise and Drift in Displacement Calculation
   • Despite smoothing, residual noise in acceleration data caused drift in velocity and displacement estimates. For future improvements, advanced filters like the Kalman filter should be explored to address this drift.
2. Temperature and Environmental Sensitivity
   • Observations showed minor variations in IMU data under different temperature conditions. Future work could involve temperature-based calibration to further improve accuracy.

Next Steps

1. Implement Advanced Filtering (Kalman Filter): Integrating a Kalman filter could help reduce drift by combining the current estimate with previous estimates and error measurements, leading to more reliable displacement values.
2. App Enhancement for Data Visualization: Adding a graphing feature in the app to display trends over time could provide users with better insights into changes in acceleration, velocity, and displacement.

Conclusion

This project successfully calculated and transmitted displacement data from an IMU via an ESP32 to a mobile app. While preliminary results are promising, further improvements in filtering and alternative communication methods could enhance both the accuracy of displacement estimation and the reliability of data transmission. Integrating advanced filtering and refining the app’s user interface for better visualization are recommended next steps.

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.

Next Steps and Action Items:

1. Bosch IMU Configuration
   • Goal: Achieve precise and reliable sensor readings by optimizing the Bosch IMU configuration to match the motion requirements of the insole.
   • Tasks:
     • Adjust Sampling Rates and Sensitivity: Experiment with different sampling rates to balance accuracy with response time. Set the accelerometer and gyroscope sensitivity levels based on anticipated user movements and required data precision.
     • Power Management Settings: Configure the Bosch IMU’s power-saving features to optimize battery usage without compromising data accuracy, essential for wearability and long-term use.
     • Data Transmission Protocol: Fine-tune data handling settings to ensure smooth transmission over BLE with minimal latency or packet loss.
2. Kalman Filter Design and Implementation
   • Goal: Build a Kalman filter to process the insole’s IMU data, allowing more accurate measurement of position and orientation by filtering out unwanted noise and stabilizing output.
   • Tasks:
     • Understanding the Basics: Begin with a detailed study of Kalman filtering principles, including understanding state estimation, prediction, and update phases. Use MATLAB or Python for initial simulations to experiment with filter parameters.
     • Develop State Equations: Define equations for the insole system’s states (e.g., position, velocity, orientation) to establish the Kalman filter model.
     • Tuning Process: Test and adjust the Kalman filter’s parameters (e.g., process noise and measurement noise covariance matrices) using sample data. The tuning process will be iterative, aiming to achieve minimal deviation while avoiding excessive response lag.
     • Implementation in ESP32: After verification in simulation, implement the filter on the ESP32. Monitor real-time performance and compare results to simulated outputs.
3. Minimizing Sensor Drift and Noise
   • Goal: Reduce drift and noise from the IMU, which can accumulate over time and lead to significant inaccuracies.
   • Tasks:
     • High-Pass and Low-Pass Filtering: Apply high-pass filters to isolate rapid motion changes (e.g., foot strikes) and low-pass filters to stabilize slow changes (e.g., orientation). Fine-tune cutoff frequencies based on motion characteristics.
     • Drift Correction Techniques: Use complementary filtering alongside the Kalman filter to continuously correct drift, especially for gyroscope readings.
     • Calibration and Testing: Regularly recalibrate the IMU, especially in varying environmental conditions, to account for temperature and magnetic interference.
     • MATLAB Simulations for Error Analysis: Use MATLAB to model common sensor errors and adjust filtering techniques accordingly. This will help in refining drift correction algorithms before implementation on the actual device.

Accomplishments This Week:

• System Architecture: Completed a detailed design for the insole, integrating the IMU, pressure pad, BLE module, and LIPO battery. Outlined data flow and real-time transmission via ESP32.
• Data Collection and Processing: Documented data handling methods, including preprocessing and filtering using MATLAB to minimize noise.
• Bluetooth: communication in Flutter, and explored graphical representation libraries.
• Validation Plan: Developed a testing strategy to assess system accuracy, response time, and battery efficiency.

Progress Status:

• Behind Schedule: Delays in Bluetooth connectivity and data flow organization. Additional effort planned to catch up.

Significant Risks:

• Bluetooth Stability: Addressing communication issues with tests and alternative BLE options.
• Data Handling: Optimizing real-time processing methods to avoid latency.

Updated Schedule:

• This Week: Complete Bluetooth setup and file system design.
• Upcoming Weeks: Finalize integration and start implementing data visualization.

Part A: Global Factors (Written by Reva Poddar)

The motion-tracking insole addresses a global need for improved gait analysis as well as injury prevention across various demographics. The solution caters to athletes and individuals undergoing physical therapy, as well as everyday users looking to monitor and enhance their movement patterns. With the rise in remote health monitoring and wearable technology adoption worldwide, the insole offers a universally accessible tool that supports different regions and user groups, even those who are not technologically inclined. The app’s compatibility with both iOS and Android devices ensures it can be used across diverse device ecosystems, making it suitable for a broad audience outside academic and local communities.

Part B: Cultural Factors (Written by Vansh Mantri)

Cultural considerations are crucial in designing the insole, as attitudes towards exercise, rehabilitation, and technology use vary widely. In communities that prioritize fitness and sports, the insole can be a tool for optimizing athletic performance. In cultures where traditional medicine or low-tech rehabilitation is preferred, the app’s user-friendly interface can help introduce the benefits of modern gait analysis without overwhelming users.

Part C: Environmental Factors (Written by Vansh mantri)

The insole is designed with sustainability in mind, promoting environmental considerations in multiple ways. By helping users prevent injuries, the need for prolonged physical therapy or medical treatments, which have environmental impacts due to resource and energy use, is reduced. The hardware components are selected for durability, minimizing electronic waste through fewer replacements.

 

The objective of the design report was to document the progress and detailed planning for the development of a motion-tracking insole. This report aimed to present the system architecture, data collection methodology, and validation strategy.

Tasks Completed:

1. System Architecture Design:
   Created a comprehensive architecture diagram detailing the insole’s key components, including the IMU (Accelerometer + Gyroscope), Pressure Pad, BLE module, and rechargeable LIPO battery. Specified the data flow between these components, with an emphasis on integrating data collection and real-time transmission using the ESP32.
2. Data Collection and Processing Methodology:
   Documented the methodology for data collection, including the specific configurations for the Bosch IMU. This section also included steps for data preprocessing, error correction, and signal filtering to minimize sensor drift and saturation using MATLAB simulations.
3. Hardware Integration and Prototyping:
   Outlined the hardware integration process and included initial tests conducted using an Arduino prototype. Summarized findings from these tests to support the decision to transition to ESP32 for final implementation.
4. Validation and Testing Approach:
   Described the validation strategy, which includes bench tests for individual components and integrated tests to assess the system’s overall performance. Focused on planned methods to evaluate accuracy, response time, and battery efficiency.
5. Challenges and Risk Assessment:
   Highlighted technical challenges faced during prototyping, including sensor noise issues and BLE connectivity stability. Conducted a risk assessment to identify potential project bottlenecks and proposed contingency plans.
6. Formatting and Presentation:
   Structured the report to ensure it meets professional standards, with sections including an executive summary, system specifications, diagrams, and tables for data presentation.