A Wearable Gait Monitoring System for 17 Gait Parameters Based on Computer Vision

We demonstrate a shoe-mounted gait monitoring system capable of tracking up to 17 gait parameters, including step length, step time, stride velocity, and among others. The system employs a stereo camera mounted on one shoe to track a marker placed on the opposite shoe, enabling the estimation of spatial gait parameters. Additionally, a force sensitive resistor (FSR) affixed to the heel of the shoe, combined with a custom-designed algorithm, is utilized to measure temporal gait parameters. Through testing on multiple participants and comparison with a gait mat, our gait monitoring system exhibited excellent performance, with all 17 gait parameters exceeding 93.61% in measurement accuracy. The system also demonstrated a low drift of 4.89% during long walking sessions. A gait identification task conducted on participants using a trained transformer model achieved 95.7% accuracy on the dataset collected by our system, indicating the potential of our system to collect long sequence, varied gait data suitable for training current large language models (LLMs). The system is cost-effective, user-friendly, and well-suited for real-life measurements.