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Related Experiment Video

Updated: Sep 11, 2025

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

Pipeline Validation for Rodent Gait Analysis Using deeplabcut.

L Savannah Dewberry¹, Carlos Cruz², Kaitlin Southern²

¹Department of Anesthesiology, School of Medicine, Duke University, Durham, NC 27710.

Journal of Biomechanical Engineering

|August 13, 2025

Summary

This summary is machine-generated.

DeepLabCut (DLC) and Automatic Gait Analysis Through Hues and Areas (AGATHA) were integrated for rat gait analysis. Combining DLC and AGATHA provides complementary spatiotemporal, dynamic, and kinematic gait measures.

Keywords:

gait analysis markerless pose estimation neural networks

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Last Updated: Sep 11, 2025

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Area of Science:

Biomechanical analysis
Animal locomotion studies
Computational neuroscience

Background:

Automatic Gait Analysis Through Hues and Areas (AGATHA) analyzes spatiotemporal gait adaptations.
AGATHA lacks the capability to measure kinematic variables like joint angles.
DeepLabCut (DLC) is designed for precise measurement of kinematic variables.

Purpose of the Study:

To integrate DeepLabCut (DLC) with the Automatic Gait Analysis Through Hues and Areas (AGATHA) algorithm.
To compare gait variables measured by DLC and AGATHA.
To evaluate the combined utility of DLC and AGATHA for comprehensive gait analysis.

Main Methods:

Hand digitization of high-speed videos to train DLC for automated marker tracking.
Development of a MATLAB pipeline to calculate gait parameters (toe height, back angle, midfoot angle) from DLC coordinates.
Analysis of 418 videos of Sprague-Dawley rats using both DLC and AGATHA.

Main Results:

DLC exhibited higher variability than AGATHA for velocity, hind limb duty factor, stride length, and step width.
DLC successfully measured kinematic variables (joint angles) absent in AGATHA.
AGATHA provided spatiotemporal and dynamic measures not obtainable by DLC.

Conclusions:

DLC and AGATHA offer complementary datasets for gait analysis.
The choice between using AGATHA alone, DLC alone, or both depends on the specific gait variables required.
Integrated DLC-AGATHA analysis enhances the scope of gait research by combining kinematic and spatiotemporal/dynamic measures.