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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

Human identification using temporal information preserving gait template.

Chen Wang¹, Junping Zhang, Liang Wang

¹Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China. chen.wang0517@gmail.com

IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 28, 2011

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This study introduces the Chrono-Gait Image (CGI), a novel gait recognition template that preserves temporal information lost in traditional Gait Energy Images (GEI). CGI enhances human identification accuracy and robustness in complex environments.

Area of Science:

Computer Vision
Biometrics
Pattern Recognition

Background:

Gait Energy Image (GEI) is a common template for gait recognition.
GEI templates discard crucial temporal information from gait sequences, limiting recognition performance.
Accurate human identification via gait requires preserving temporal dynamics.

Purpose of the Study:

To develop a novel temporal template for gait recognition that overcomes GEI limitations.
Introduce the Chrono-Gait Image (CGI) to effectively capture temporal gait information.
Enhance the robustness and efficiency of gait recognition systems.

Main Methods:

Extracting contours from individual gait frames.
Encoding gait contour images using a multichannel mapping function.

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Last Updated: May 26, 2026

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

Compositing encoded contours into a single Chrono-Gait Image (CGI).

Developing CGI-based templates preserving temporal information using gait periods and contour distortion.

Main Results:

The proposed CGI-based approach demonstrates competitive performance in gait recognition.
CGI templates show improved robustness against complex environmental factors.
Experimental results on benchmark databases validate the effectiveness of the CGI approach.

Conclusions:

The Chrono-Gait Image (CGI) effectively preserves temporal information crucial for gait recognition.
CGI offers a robust and efficient alternative to existing gait recognition templates.
This novel approach advances the field of human identification by gait.