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Updated: Mar 19, 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

Quantifying prosthetic gait deviation using simple outcome measures.

Lauren Kark¹, Ross Odell¹, Andrew S McIntosh¹

¹Lauren Kark, Ross Odell, Anne Simmons, Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia.

World Journal of Orthopedics

|June 24, 2016

Summary

This summary is machine-generated.

Simple walking tests like the six-minute walk test can effectively measure prosthetic gait deviation in lower-limb amputees, offering a practical alternative to complex three-dimensional gait analysis.

Keywords:

Amputation Functional outcomes Gait deviation Prosthesis Receiver operating characteristic curve Regression

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Last Updated: Mar 19, 2026

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

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Published on: March 4, 2018

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Published on: March 23, 2019

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Published on: April 18, 2011

Area of Science:

Biomechanics
Prosthetics
Gait Analysis

Background:

Quantifying prosthetic gait deviation typically requires sophisticated three-dimensional gait analysis (3DGA).
There is a need for simpler, more accessible methods to assess gait deviations in individuals with lower-limb prostheses.

Purpose of the Study:

To develop a simplified set of outcome measures for quantifying prosthetic gait deviation.
To establish an alternative to 3DGA for assessing gait in amputees.

Main Methods:

Eight transfemoral and 12 transtibial amputees, along with 28 able-bodied controls, underwent 3DGA, timed-up-and-go, and the six-minute walk test (6MWT).
Gait deviation index (GDI) from 3DGA was regressed against other measures, including the Prosthesis Evaluation Questionnaire.

Main Results:

Step-length (SL), self-selected walking speed (SSWS), and 6MWT distance (6MWD) significantly correlated with GDI.
6MWD emerged as the strongest predictor of GDI, followed by SL and SSWS.
Including self-reported mobility and prosthetic utility data improved the predictive accuracy of regression models.

Conclusions:

Simple measures like 6MWD, SL, and SSWS can effectively quantify prosthetic gait deviation.
This approach provides a practical alternative to conducting full 3DGA for gait assessment in amputees.