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Updated: Jul 1, 2025

Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents

Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents

Published on: July 6, 2022

Markerless gait analysis through a single camera and computer vision.

Hanwen Wang¹, Bingyi Su¹, Lu Lu¹

¹Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA.

Journal of Biomechanics

|March 2, 2024

Summary

This summary is machine-generated.

Markerless gait analysis using computer vision shows promise for assessing health, correlating well with marker-based systems for most parameters. However, accuracy for clinical use needs improvement, especially concerning camera angles and distance.

Keywords:

Deep neural networks Gait parameters Joint kinematics Lower extremities Motion tracking

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Last Updated: Jul 1, 2025

Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents

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Published on: July 6, 2022

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Published on: March 4, 2018

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:

Biomechanics
Computer Vision
Human Motion Analysis

Background:

Quantitative gait analysis provides health insights.
Markerless human pose estimation offers potential for gait parameter extraction from videos.

Purpose of the Study:

Compare markerless and marker-based gait analysis for lower extremity kinematics and spatiotemporal parameters.
Evaluate the influence of camera viewing angles and distances on markerless method accuracy.

Main Methods:

Single-camera markerless human pose estimation.
Marker-based motion tracking system.
Comparison across a healthy population.

Main Results:

Strong correlations (Rxy > 0.75) between markerless and marker-based methods for most spatiotemporal gait parameters.
High correlations (Rxy > 0.8) for hip and knee joint kinematics.
Significant impact of camera viewing angles and distances on accuracy.

Conclusions:

Markerless gait analysis is a viable alternative for general applications where markers are impractical.
Current markerless method accuracy is insufficient for clinical diagnosis and requires further development.
Future research should focus on improving accuracy and exploring applications in pathological gait analysis.