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.
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.
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.
