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

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 durin...

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Autores principales: Darbandi, Hamed, Serra Bragança, Filipe, van der Zwaag, Berend Jan, Voskamp, John, Gmel, Annik Imogen, Haraldsdóttir, Eyrún Halla, Havinga, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865839/
https://www.ncbi.nlm.nih.gov/pubmed/33530288
http://dx.doi.org/10.3390/s21030798
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author Darbandi, Hamed
Serra Bragança, Filipe
van der Zwaag, Berend Jan
Voskamp, John
Gmel, Annik Imogen
Haraldsdóttir, Eyrún Halla
Havinga, Paul
author_facet Darbandi, Hamed
Serra Bragança, Filipe
van der Zwaag, Berend Jan
Voskamp, John
Gmel, Annik Imogen
Haraldsdóttir, Eyrún Halla
Havinga, Paul
author_sort Darbandi, Hamed
collection PubMed
description 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.
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spelling pubmed-78658392021-02-07 Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach Darbandi, Hamed Serra Bragança, Filipe van der Zwaag, Berend Jan Voskamp, John Gmel, Annik Imogen Haraldsdóttir, Eyrún Halla Havinga, Paul Sensors (Basel) Article 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. MDPI 2021-01-26 /pmc/articles/PMC7865839/ /pubmed/33530288 http://dx.doi.org/10.3390/s21030798 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Darbandi, Hamed
Serra Bragança, Filipe
van der Zwaag, Berend Jan
Voskamp, John
Gmel, Annik Imogen
Haraldsdóttir, Eyrún Halla
Havinga, Paul
Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach
title Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach
title_full Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach
title_fullStr Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach
title_full_unstemmed Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach
title_short Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach
title_sort using different combinations of body-mounted imu sensors to estimate speed of horses—a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865839/
https://www.ncbi.nlm.nih.gov/pubmed/33530288
http://dx.doi.org/10.3390/s21030798
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