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A Video Processing Pipeline for Equine Biomechanical Parameters Extraction and Gait Analysis

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2021 ASABE Annual International Virtual Meeting  2100942.(doi:10.13031/aim.202100942)
Authors:   Vinika Gupta, Ariana West, Yin Bao, Samantha A Brooks, Elizabeth A Staiger
Keywords:   Click here to enter keywords and key phrases, separated by commas, with a period at the end

Abstract. Gait analysis plays a pivotal role in determining and improving equine biomechanical performance. Currently, equine gait analysis requires attaching markers or IMU sensors to a horse for motion capture, which is low-throughput and may affect horse behavior. In this study, we evaluated the utility of deep learning-based video processing as a marker-less motion capture method for equine gait analysis. A video dataset of horses in locomotion was created labeling 21 equine body parts in 20 frames pulled at random from each of 15 videos. Body part tracking was realized by DeepLabCut, which achieved highly accurate tracking results. In addition, we developed robust algorithms to extract two biomechanical parameters based on the body part tracking results, stride length and stance time. The proposed system was able to correctly detect 92% of the strides and 95% of the stances. Subsequently, satisfactory accuracies were found for the automatically extracted stride length (R2 = 0.80) and stance time (R2 = 0.81). The developed video processing pipeline has promising potential to become a convenient and efficient analytics tool for animal scientists and veterinarians to study the genetic control of equine locomotion and diagnose musculoskeletal problems, respectively.

Keywords. biomechanics, convolutional neural network, deep learning, gait analysis, horse, lameness, locomotion

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