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Updated: Feb 6, 2026
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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
Published on: November 7, 2014
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Human gait symmetry assessment using a depth camera and mirrors
Trong-Nguyen Nguyen1, Huu-Hung Huynh2, Jean Meunier1
1DIRO, University of Montreal, Montreal, QC, H3T 1J4, Canada.
Computers in Biology and Medicine
|August 27, 2018
Summary
This study introduces a reliable method for human gait symmetry assessment using a Time-of-Flight (ToF) depth camera and mirrors. The system accurately quantines gait symmetry across various walking types.
Area of Science:
- Biomedical Engineering
- Computer Vision
- Human Motion Analysis
Background:
- Gait symmetry is crucial for assessing neuromuscular disorders and rehabilitation progress.
- Existing methods for gait analysis often require specialized equipment or controlled environments.
- Objective and reliable gait symmetry assessment is needed for clinical and research applications.
Purpose of the Study:
- To propose and validate a novel, reliable approach for human gait symmetry assessment.
- To utilize a Time-of-Flight (ToF) depth camera and mirrors for capturing 3D gait data.
- To develop a system capable of quantifying gait symmetry indices from 3D point cloud sequences.
Main Methods:
- A system combining a ToF depth camera and two mirrors captures 3D point clouds of human gait.
- Cylindrical histograms are computed to represent posture from each point cloud.
- Histograms are divided into two sequences for the body's half-sides, and cross-correlation is applied to derive gait symmetry indices.
Main Results:
- The proposed approach demonstrated effectiveness in assessing gait symmetry across 9 different gait types.
- Quantitative gait symmetry indices were successfully generated.
- The system's performance was validated against related methods using different data types.
Conclusions:
- The developed system offers a reliable and accessible method for human gait symmetry assessment.
- The use of ToF depth cameras and mirrors provides a practical solution for capturing 3D gait data.
- This approach has potential applications in clinical diagnostics, rehabilitation monitoring, and biomechanical research.
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