biomechanics-signal-processing

A Python toolkit for analyzing FSR force-time data, filtering signals, and detecting gait events (heel strike & toe-off).

WHAT IT DOES & WHY IT MATTERS

This project implements a complete force-signal processing pipeline for gait biomechanics. It loads FSR force-time data from CSV, applies smoothing filters (Moving Average, Butterworth), computes slopes, and automatically detects heel strike and toe-off events.

This mirrors standard practice used in biomechanics research labs and wearable robotics systems.

FEATURES

• Load time-series FSR force data from CSV
• Visualize raw force-time curves
• Compute slope (rate of force development)
• Moving Average smoothing
• 4th-order Butterworth low-pass filtering
• Heel strike and toe-off detection
• Plotting filtered vs raw force signals

REPO STRUCTURE

biomechanics-signal-processing/ │ ├── CSVdata/ │ └── fsr.csv | ├── src/ │ └── force_signal_analysis.py | ├── plots/ ├── README.md ├── requirements.txt └── .gitignore

GENERATED GRAPHICS

1. Raw Force-Time Data

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2. Force Slope (Rate of Change)

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3. Moving Average Smoothing (Window = 3)

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4. Butterworth Low-Pass Filter (8 Hz, 4th Order)

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5. Heel Strike & Toe-Off Detection (Filtered Force)

LICENSE

This project is licensed under the MIT License.

Future Work

• Support for additional sensor types (e.g., IMU data)
• Real-time streaming or live plotting
• Automated parameter tuning for Butterworth cutoff
• Integration with control systems