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:
- Stride Length Calculation: Developing a reliable method to compute stride length from footstep detection data, accounting for individual variations in stride dynamics.
- Real-Time Integration: Ensuring the processing algorithms operate efficiently within the app without impacting performance.
- Edge Cases: Simulated datasets highlighted specific scenarios, such as abrupt movement changes, that require further refinement in the detection algorithms.
Next Steps:
- 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.
- Refine the detection algorithms to improve robustness in handling edge cases and variability in user gaits.
- Seek feedback from initial users to identify pain points and guide further optimization of both detection and stride length computation.