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Updated: May 17, 2025

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

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

Jiangang Chen¹, Yung-Hong Sun¹, Kristen A Pickett²

¹Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706 USA.

IEEE Transactions on Instrumentation and Measurement

|May 15, 2025

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A novel shoe-mounted system accurately tracks 17 gait parameters using cameras and sensors. This cost-effective gait monitoring technology achieves high accuracy and is suitable for real-world applications and AI training.

Area of Science:

Biomechanics
Wearable Technology
Human Motion Analysis

Background:

Gait analysis is crucial for understanding human locomotion and diagnosing movement disorders.
Existing gait monitoring systems can be expensive, complex, or limited in real-world applicability.

Purpose of the Study:

To develop and validate a cost-effective, user-friendly shoe-mounted system for comprehensive gait parameter monitoring.
To assess the system's accuracy, reliability, and suitability for collecting data for advanced AI models.

Main Methods:

Utilized a stereo camera on one shoe to track a marker on the opposite shoe for spatial gait parameters.
Integrated a force sensitive resistor (FSR) in the heel with a custom algorithm for temporal gait parameters.
Validated system performance against a gait mat across multiple participants.

Keywords:

Computer Vision Gait Identification Gait Monitoring Stereo Camera Transformer Model Wearable Device

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Last Updated: May 17, 2025

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Main Results:

The system accurately tracked 17 gait parameters with >93.61% measurement accuracy.
Demonstrated low drift (4.89%) during extended walking sessions.
Achieved 95.7% accuracy in a gait identification task using a transformer model.

Conclusions:

The shoe-mounted system offers a highly accurate and reliable method for gait monitoring.
The collected data is suitable for training AI models, including large language models (LLMs), for gait recognition.
The system's cost-effectiveness and ease of use make it ideal for real-life gait measurements.