Visual Gait Lab: A User-Friendly Approach to Gait Analysis

J Neurosci Methods. 2020 Jul 15;341:108775. doi: 10.1016/j.jneumeth.2020.108775. Epub 2020 May 16.


Background: Gait analysis forms a critical part of many lab workflows, ranging from those interested in preclinical neurological models to others who use locomotion as part of a standard battery of tests. Unfortunately, while paw detection can be semi-automated, it becomes generally a time-consuming process with error corrections. Improvement in paw tracking would aid in better gait analysis performance and experience.

New method: Here we show the use of Visual Gait Lab (VGL), a high-level software with an intuitive, easy to use interface, that is built on DeepLabCut™. VGL is optimized to generate gait metrics and allows for quick manual error corrections. VGL comes with a single executable, streamlining setup on Windows systems. We demonstrate the use of VGL to analyze gait.

Results: Training and evaluation of VGL were conducted using 200 frames (80/20 train-test split) of video from mice walking on a treadmill. The trained network was then used to visually track paw placements to compute gait metrics. These are processed and presented on the screen where the user can rapidly identify and correct errors.

Comparison with existing methods: Gait analysis remains cumbersome, even with commercial software due to paw detection errors. DeepLabCut™ is an alternative that can improve visual tracking but is not optimized for gait analysis functionality.

Conclusions: VGL allows for gait analysis to be performed in a rapid, unbiased manner, with a set-up that can be easily implemented and executed by those without a background in computer programming.

Keywords: DeepLabCut™; Gait analysis; Gait tracking system; Motor control; Mouse locomotion.