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Article

Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

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Pervasive Systems Group, Department of Computer Science, University of Twente, 7522 NB Enschede, The Netherlands
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Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, 3584 CM Utrecht, The Netherlands
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Inertia Technology B.V., 7521 AG Enschede, The Netherlands
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Rosmark Consultancy, 6733 AA Wekerom, The Netherlands
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Equine Department, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland
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Agroscope—Swiss National Stud Farm, Les Longs-Prés, 1580 Avenches, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editor: Winson Lee
Sensors 2021, 21(3), 798; https://doi.org/10.3390/s21030798
Received: 17 December 2020 / Revised: 8 January 2021 / Accepted: 20 January 2021 / Published: 26 January 2021
(This article belongs to the Special Issue Wearable Sensors for Biomechanics Applications)
Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML. View Full-Text
Keywords: inertial measurement unit; machine learning; breed; gait; feature extraction inertial measurement unit; machine learning; breed; gait; feature extraction
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MDPI and ACS Style

Darbandi, H.; Serra Bragança, F.; van der Zwaag, B.J.; Voskamp, J.; Gmel, A.I.; Haraldsdóttir, E.H.; Havinga, P. Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach. Sensors 2021, 21, 798. https://doi.org/10.3390/s21030798

AMA Style

Darbandi H, Serra Bragança F, van der Zwaag BJ, Voskamp J, Gmel AI, Haraldsdóttir EH, Havinga P. Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach. Sensors. 2021; 21(3):798. https://doi.org/10.3390/s21030798

Chicago/Turabian Style

Darbandi, Hamed, Filipe Serra Bragança, Berend J. van der Zwaag, John Voskamp, Annik I. Gmel, Eyrún H. Haraldsdóttir, and Paul Havinga. 2021. "Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach" Sensors 21, no. 3: 798. https://doi.org/10.3390/s21030798

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