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Related Experiment Video

Updated: Feb 2, 2026

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

Gait Evaluation Using Procrustes and Euclidean Distance Matrix Analysis.

Arif Reza Anwary, Hongnian Yu, Michael Vassallo

IEEE Journal of Biomedical and Health Informatics

|November 13, 2018

Summary

This study introduces a novel gait evaluation system using an Android app and inertial sensors to analyze gait features. The system effectively distinguishes gait patterns between young and older adults, aiding in rehabilitation and disease assessment.

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Area of Science:

Biomechanics
Medical Engineering
Data Science

Background:

Objective gait assessment is crucial for patient treatment and rehabilitation across various diseases.
Existing methods may lack precision or real-world applicability.
Developing accessible, accurate gait analysis tools is a significant need.

Purpose of the Study:

To propose and validate a novel gait evaluation system using Procrustes and Euclidean distance matrix analysis.
To develop an Android application for real-time data collection from inertial measurement units.
To analyze and differentiate gait features between young and older adults.

Main Methods:

Developed an Android app for synchronous collection of accelerometer and gyroscope data via Bluetooth from two IMU sensors.
Utilized Generalized Procrustes Analysis (GPA) to establish a Normal Mean Gait Shape (NMGS) from young subjects.
Applied Ordinary Procrustes Analysis (OPA) and various distance metrics (Riemannian, Procrustes, RMSD) to compare individual gaits against the NMGS.
Performed t-tests to statistically validate gait shape differences and estimated abnormality degrees.

Main Results:

The system successfully collected and analyzed real-world gait data, including stride, step, stance, and swing features.
Significant statistical differences in gait shape were identified between young and older adult subjects.
The method demonstrated effectiveness in estimating the degree of abnormality for individual gait features.

Conclusions:

The proposed gait evaluation system, leveraging Procrustes analysis and mobile technology, provides an objective and effective method for assessing gait.
The system can differentiate between age-related gait variations, offering potential for clinical applications in diagnosis and rehabilitation.
The visualization of gait features aids in understanding and addressing abnormalities.