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Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning

Braganca, F. M. Serra and Broome, S. and Rhodin, Marie and Björnsdottir, S. and Gunnarsson, V. and Voskamp, J. P. and Persson Sjödin, Emma and Back, W. and Lindgren, Gabriella and Novoa, Miguel and Roepstorff, C. and van der Zwaag, B. J. and Van Weeren, P. R. and Hernlund, Elin (2020). Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning. Scientific Reports. 10 , 17785
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Abstract

For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms.

Authors/Creators:Braganca, F. M. Serra and Broome, S. and Rhodin, Marie and Björnsdottir, S. and Gunnarsson, V. and Voskamp, J. P. and Persson Sjödin, Emma and Back, W. and Lindgren, Gabriella and Novoa, Miguel and Roepstorff, C. and van der Zwaag, B. J. and Van Weeren, P. R. and Hernlund, Elin
Title:Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning
Year of publishing :2020
Volume:10
Article number:17785
Number of Pages:9
Publisher:NATURE RESEARCH
ISSN:2045-2322
Language:English
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 403 Veterinary Science > Medical Bioscience
URN:NBN:urn:nbn:se:slu:epsilon-p-109310
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-109310
Additional ID:
Type of IDID
DOI10.1038/s41598-020-73215-9
Web of Science (WoS)000585236900044
ID Code:19760
Faculty:VH - Faculty of Veterinary Medicine and Animal Science
Department:(VH) > Dept. of Anatomy, Physiology and Biochemistry
(VH) > Dept. of Clinical Sciences
(VH) > Dept. of Animal Breeding and Genetics
Deposited By: SLUpub Connector
Deposited On:22 Dec 2020 19:23
Metadata Last Modified:15 Jan 2021 19:18

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