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

TL;DR

This paper presents a cost-effective, shoe-mounted gait monitoring system using computer vision and sensors to accurately track 17 gait parameters, suitable for real-life applications and integration with AI models.

Contribution

The authors introduce a novel wearable gait monitoring system combining stereo vision and force sensors, achieving high accuracy and long-distance stability for gait analysis.

Findings

Gait parameters measured with over 93.61% accuracy.

System demonstrated a low drift of 4.89% during long walks.

Achieved 95.7% accuracy in gait identification using a Transformer model.

Abstract

We developed a shoe-mounted gait monitoring system capable of tracking up to 17 gait parameters, including gait length, step time, stride velocity, and 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 the gait mat, the proposed gait monitoring system exhibited notable performance, with the accuracy of all measured gait parameters exceeding 93.61%. The system also demonstrated a low drift of 4.89% during long-distance walking. A gait identification task conducted on participants using a trained Transformer model achieved 95.7% accuracy on the dataset collected by the proposed system, demonstrating that our hardware has the potential to collect long-sequence gait data suitable for integration with current Large Language Models (LLMs). The system is cost-effective, user-friendly, and well-suited for real-life measurements.