Abstract
Gait analysis in cats and other animals is generally performed with custom-made or commercially developed software to track reflective markers placed on bony landmarks. This often involves costly motion tracking systems. However, deep learning, and in particular DeepLabCutTM (DLC), allows motion tracking without requiring placing reflective markers or an expensive system. The purpose of this study was to validate the accuracy of DLC for gait analysis in the adult cat by comparing results obtained with DLC and a custom-made software (Expresso) that has been used in several cat studies. Four intact adult cats performed tied-belt (both belts at same speed) and split-belt (belts operating at different speeds) locomotion at different speeds and left-right speed differences on a split-belt treadmill. We calculated several kinematic variables, such as step/stride lengths and joint angles from the estimates made by the two software and assessed the agreement between the two measurements using intraclass correlation coefficient or Lin’s concordance correlation coefficient as well as Pearson’s correlation coefficients. The results showed that DLC is at least as precise as Expresso with good to excellent agreement for all variables. Indeed, all 12 variables showed an agreement above 0.75, considered good, while nine showed an agreement above 0.9, considered excellent. Therefore, deep learning, specifically DLC, is valid for measuring kinematic variables during locomotion in cats, without requiring reflective markers and using a relatively low-cost system.
Highlights
Millennia ago, our ancestors illustrated animals in movement, as depicted in the cave of Lascaux, France
The results reported here are based on an analysis of 2,500 cycles obtained from 177 trials in four adult cats (41–47 trials per cat)
The goal of the present study was to determine if measures of gait variables obtained with DLC agree or differ with those of a custom-made software that has been used in several cat studies
Summary
Our ancestors illustrated animals in movement (e.g., hunting or fleeing), as depicted in the cave of Lascaux, France. In the 1870s, the French physician and physiologist Étienne Jules Marey invented the photographic gun and chronophotography, creating “videos” of various animals in motion, such as dogs, cats, horses, and sheep. This major technological advance for studying motion and locomotion decomposed movement into series of consecutive photographic pictures (Marey, 1873). Philippson (1905) divided the dog’s walking cycle into several phases based on joint angular excursions and their transitions from flexion to extension. In the late 1960s, Engberg and Lundberg combined, for the first time, kinematic and electromyographic data during unrestrained locomotion in cats, establishing relationships between muscle activity and changes in joint angles (Engberg and Lundberg, 1969)
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