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Related Experiment Video

Updated: May 22, 2026

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

iGAIT: an interactive accelerometer based gait analysis system.

Mingjing Yang¹, Huiru Zheng, Haiying Wang

¹School of Computing and Mathematics, University of Ulster, N. Ireland, UK.

Computer Methods and Programs in Biomedicine

|May 12, 2012

Summary

This summary is machine-generated.

This study introduces iGAIT, a MATLAB software for analyzing gait patterns from accelerometer data. It extracts 31 gait features and offers interactive analysis for diverse applications.

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Last Updated: May 22, 2026

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

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

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

Area of Science:

Biomechanics
Wearable Technology
Signal Processing

Background:

Gait analysis is crucial for understanding human movement and diagnosing conditions.
Accelerometer-based gait analysis offers a portable and accessible method.
Existing tools may lack comprehensive feature extraction or user-friendly interfaces.

Purpose of the Study:

To present iGAIT, a novel software program for detailed gait pattern analysis.
To enable extraction and visualization of numerous gait features from accelerometer data.
To provide an interactive platform for customized gait data analysis.

Main Methods:

Development of iGAIT using MATLAB with a graphical user interface.
Implementation of gait feature extraction algorithms to derive 31 distinct features.
Testing the software across different Windows operating systems and accelerometer data types.

Main Results:

iGAIT successfully extracts 31 gait features, including spatio-temporal, regularity/symmetry, and spectral metrics.
The software provides interactive tools for users to adjust analysis parameters.
Demonstrated compatibility with various accelerometer data sampled from 5 Hz to 200 Hz.

Conclusions:

iGAIT is a versatile and user-friendly software for comprehensive gait analysis using accelerometers.
The interactive features enhance the adaptability of gait data analysis.
This tool supports research and clinical applications requiring detailed gait pattern assessment.