Author: vanshm

Testing Summary

Participants:

1. Height: 6 feet, Gender: Male
2. Height: 5 feet 8 inches, Gender: Male
3. Height: 5 feet, Gender: Female

Key Objectives:

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

Findings:

1. Usability & Comfort:
   • The device was reported as easy to use, with participants quickly adapting to its setup.
   • Comfort feedback was positive, with slight material adjustments suggested for enhanced fit, particularly by the shortest participant.
2. Step-Count Refinement:
   • Improved calibration of the step-detection algorithm ensured accurate results for all participants, regardless of differences in stride length or height.
3. Firmware Updates:
   • Resolved a critical bug related to BLE transmission by ensuring the data buffer is emptied onto the SD card before transmitting via Bluetooth.
   • This update improved system reliability and data integrity.

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Additional Progress

1. Second Shoe Prototype:
   • Initiated the fabrication of the second insole prototype to enable bilateral motion analysis and further expand testing capabilities.
2. Final Presentation:
   • Drafted and refined the project’s final presentation to clearly communicate the goals, challenges, and progress to stakeholders.
   • Integrated visuals and data to effectively showcase testing results and technical milestones.

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New Tools and Knowledge Acquisition

To design, implement, and debug this project, I had to explore a range of new tools and acquire knowledge across various domains. Here’s what I learned and how I went about it:

1. Tools and Knowledge Acquired:
   • Firmware Development: Delved into SD card data management and BLE protocols to ensure seamless data transmission.
   • Hardware Integration: Worked extensively with the ESP32 and Bosch IMU, learning to minimize sensor drift and optimize data processing.
   • MATLAB Simulations: Conducted simulations to fine-tune system parameters, particularly to minimize sensor saturation.
2. Learning Strategies:
   • YouTube Tutorials: When confronted with unfamiliar concepts, I relied heavily on YouTube for quick, visual explanations.
   • GitHub Repositories: I explored open-source projects to better understand best practices and avoid reinventing the wheel.
   • Online Forums: Platforms like Stack Overflow and specialized robotics forums were invaluable for troubleshooting specific issues.

One key insight I gained was the importance of understanding the question before attempting to answer it. A problem that is well-defined is already half-solved. By breaking down complex challenges into smaller, digestible parts and focusing on clear problem statements, I could systematically work toward effective solutions.

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Next Steps

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

We have successfully developed the MVP (Minimum Viable Product) for one shoe as a demonstration unit. The following progress highlights key developments and implementations:

1. FreeRTOS Implementation: The ESP32 is now running FreeRTOS, enabling efficient multitasking and real-time performance for the motion-sensing system.
2. Yost IMU Integration: The Yost IMU has been integrated using HSPI, providing accurate motion data capture.
3. SD Card Functionality: An SD card module is operational using SPI (VSPI) on the ESP32, enabling secure data storage for recorded motion metrics.

This iteration demonstrates significant advancements in system stability, data reliability, and real-time processing, marking a major milestone for the project. The MVP will be tested during the demo to validate its performance under real-world conditions.

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.

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.

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.

1. Finalized Hardware Components:

• Inertial Measurement Unit (IMU): Selected an IMU that includes both an accelerometer and gyroscope. This will be used for real-time motion tracking.
• Pressure Pad: Chosen for accurate pressure detection and user feedback.
• Bluetooth Low Energy (BLE) Module: Decided on a BLE module for low-power wireless communication between the insole and external devices.
• Battery: Calculated power requirements for the system and confirmed the use of a rechargeable LIPO battery. These calculations have been documented and posted in the design report.
• ESP32 Selection: After meeting with the CLIMB team (who are working on a similar project), we decided to use an ESP32 microcontroller for its low power consumption, integrated BLE, and ease of interfacing with the sensors and battery.

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2. Collaboration with CLIMB Team:

• Met with the CLIMB team to discuss design decisions, as they are using a similar framework. We shared insights into using the ESP32 microcontroller and discussed battery management with LIPO rechargeable batteries. This collaborative discussion helped solidify our design decisions regarding energy efficiency and sensor integration.

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3. IMU Testing and Simulation:

• Successfully connected an Arduino to the Bosch IMU sensor to begin collecting data.
• Conducted a preliminary test and ran a simulation in MATLAB to analyze sensor drift and saturation. The simulation helped identify ways to minimize these issues, which will be addressed in future sensor calibration and firmware development.

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Next Steps:

• Continue sensor calibration and integration with the ESP32.
• Finalize the communication protocol between the insole and external devices.
• Work on refining the simulation model to better predict and reduce sensor drift.

Status Report: Component Selection and Design Finalization

Task Overview:
This phase involves selecting the appropriate components (sensors, power systems, connectivity modules) and finalizing the overall design of the motion-tracking sole. The goal is to ensure the technical specifications meet the operational requirements outlined in previous discussions.

Progress Summary:

1. Component Selection:
– IMU (Accelerometers & Gyroscopes):
– We have chosen the Bosch IMU for tracking both linear and angular motion, providing a ±16g accelerometer range and a ±2000°/s gyroscope range, ensuring sufficient accuracy for our use cases.
– This selection is subject to change as we finalize our design report and validate whether these specifications fully meet the project’s needs.
– Pressure Sensors:
– Opted for high-resolution pressure pads with 8-bit resolution for precise foot pressure mapping.
– We are using multiple pressure pads (forefoot, midfoot, and heel) to provide granular data on foot movement and weight distribution. As we complete our design report, these configurations may be adjusted based on further testing.
– Microcontroller (ESP32):
– We’ve selected the ESP32 microcontroller, which offers I2C communication to interface with the IMU and Bluetooth Low Energy (BLE) for data transmission to the mobile app.
– The ESP32 was chosen due to its integrated BLE and Wi-Fi capabilities, making it ideal for both communication and sensor data handling in our compact design.
– Power Supply:
– A 200mAh rechargeable lithium-polymer battery has been selected, capable of supporting at least 3 hours of continuous use. This is subject to refinement based on the final power consumption metrics from our design report and initial testing.
– Connectivity:
– We’ve decided on Bluetooth Low Energy (BLE) 5.0 for efficient, low-power data transmission, ensuring smooth communication with mobile devices and minimizing power consumption.

2. Design Finalization:
– Insole Design:
– Proceeding with a removable insole design for flexibility and broader market appeal.
– Sensor placement has been optimized at key foot points (heel, midfoot, forefoot) to balance performance and user comfort, subject to minor adjustments as we finalize the design.
– Material Selection:
– Lightweight, moisture-wicking materials have been chosen for comfort and durability, though this will be finalized following further user testing.
– Durability Considerations:
– The insole design will include a waterproof rating of IP68, ensuring long-lasting performance in varied environments.

Challenges Encountered:
– Battery Life Optimization:
Continuing to optimize power consumption, particularly around balancing sensor performance with battery efficiency, as this will affect the final design.

– Sensor Calibration:
Ongoing work on refining calibration settings to ensure accurate data collection across different user cases and environments.

Next Steps:
– Complete the design report to finalize component selections and specifications.
– Begin prototyping with selected components for initial testing and validation.
– Continue optimizing power circuits for extended battery life.

Design Review Presentation:
– I am currently preparing a design review presentation that will summarize the component selection, design choices, and rationale behind the decisions. This presentation, which will be ready by the end of the week, will serve as a key milestone to finalize the hardware and software integration plan.

Conclusion:
We are progressing well with component selection and design finalization, though all numbers and specifications are subject to change as we complete the design report. The Bosch IMU and ESP32 microcontroller have been chosen for their respective roles in data collection and transmission, and the design review presentation will provide further insights into our development process. Our next phase will focus on prototyping and testing.