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Updated: Nov 10, 2025

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 recognition using a few gait frames.

Lingxiang Yao¹, Worapan Kusakunniran², Qiang Wu¹

¹School of Electrical and Data Engineering, University of Technology Sydney, Sydney, Australia.

Peerj. Computer Science

|April 5, 2021

Summary

This summary is machine-generated.

This study enhances gait recognition for surveillance by generating more gait frames to improve accuracy with limited data. The new two-branch network effectively uses original and generated frames for reliable individual identification.

Keywords:

Gait recognition Limited gait frames Silhouette Skeleton Two-branch network

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Last Updated: Nov 10, 2025

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

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

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Published on: August 3, 2019

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

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Published on: September 14, 2017

Area of Science:

Computer Science
Biometrics
Artificial Intelligence

Background:

Gait recognition is a key biometric for video surveillance, enabling remote identification without subject cooperation.
Existing gait recognition methods struggle with limited input frames, significantly degrading performance in real-world scenarios due to factors like occlusion and illumination.

Purpose of the Study:

To improve gait recognition accuracy when limited gait frames are available.
To address the performance drop in gait recognition systems caused by insufficient input data.

Main Methods:

Developed three strategies to generate additional gait frames and mitigate generalization errors from limited data.
Proposed a novel two-branch network to create robust gait representations from both original and generated frames.

Main Results:

Experimental validation demonstrated reliable gait recognition performance even with a reduced number of input frames.
The proposed method effectively handles scenarios with insufficient gait data, outperforming existing approaches.

Conclusions:

The developed strategies and two-branch network significantly enhance gait recognition robustness under data limitations.
This research offers a practical solution for real-world surveillance applications requiring accurate gait identification with variable frame availability.