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Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors

For gait classification, hoof-on and hoof-off events are fundamental locomotion characteristics of interest. These events can be measured with inertial measurement units (IMUs) which measure the acceleration and angular velocity in three directions. The aim of this study was to present two algorithm...

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Autores principales: Tijssen, M., Hernlund, E., Rhodin, M., Bosch, S., Voskamp, J. P., Nielen, M., Serra Braganςa, F. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7269263/
https://www.ncbi.nlm.nih.gov/pubmed/32492034
http://dx.doi.org/10.1371/journal.pone.0233266
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author Tijssen, M.
Hernlund, E.
Rhodin, M.
Bosch, S.
Voskamp, J. P.
Nielen, M.
Serra Braganςa, F. M.
author_facet Tijssen, M.
Hernlund, E.
Rhodin, M.
Bosch, S.
Voskamp, J. P.
Nielen, M.
Serra Braganςa, F. M.
author_sort Tijssen, M.
collection PubMed
description For gait classification, hoof-on and hoof-off events are fundamental locomotion characteristics of interest. These events can be measured with inertial measurement units (IMUs) which measure the acceleration and angular velocity in three directions. The aim of this study was to present two algorithms for automatic detection of hoof-events from the acceleration and angular velocity signals measured by hoof-mounted IMUs in walk and trot on a hard surface. Seven Warmblood horses were equipped with two wireless IMUs, which were attached to the lateral wall of the right front (RF) and hind (RH) hooves. Horses were walked and trotted on a lead over a force plate for internal validation. The agreement between the algorithms for the acceleration and angular velocity signals with the force plate was evaluated by Bland Altman analysis and linear mixed model analysis. These analyses were performed for both hoof-on and hoof-off detection and for both algorithms separately. For the hoof-on detection, the angular velocity algorithm was the most accurate with an accuracy between 2.39 and 12.22 ms and a precision of around 13.80 ms, depending on gait and hoof. For hoof-off detection, the acceleration algorithm was the most accurate with an accuracy of 3.20 ms and precision of 6.39 ms, independent of gait and hoof. These algorithms look highly promising for gait classification purposes although the applicability of these algorithms should be investigated under different circumstances, such as different surfaces and different hoof trimming conditions.
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spelling pubmed-72692632020-06-10 Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors Tijssen, M. Hernlund, E. Rhodin, M. Bosch, S. Voskamp, J. P. Nielen, M. Serra Braganςa, F. M. PLoS One Research Article For gait classification, hoof-on and hoof-off events are fundamental locomotion characteristics of interest. These events can be measured with inertial measurement units (IMUs) which measure the acceleration and angular velocity in three directions. The aim of this study was to present two algorithms for automatic detection of hoof-events from the acceleration and angular velocity signals measured by hoof-mounted IMUs in walk and trot on a hard surface. Seven Warmblood horses were equipped with two wireless IMUs, which were attached to the lateral wall of the right front (RF) and hind (RH) hooves. Horses were walked and trotted on a lead over a force plate for internal validation. The agreement between the algorithms for the acceleration and angular velocity signals with the force plate was evaluated by Bland Altman analysis and linear mixed model analysis. These analyses were performed for both hoof-on and hoof-off detection and for both algorithms separately. For the hoof-on detection, the angular velocity algorithm was the most accurate with an accuracy between 2.39 and 12.22 ms and a precision of around 13.80 ms, depending on gait and hoof. For hoof-off detection, the acceleration algorithm was the most accurate with an accuracy of 3.20 ms and precision of 6.39 ms, independent of gait and hoof. These algorithms look highly promising for gait classification purposes although the applicability of these algorithms should be investigated under different circumstances, such as different surfaces and different hoof trimming conditions. Public Library of Science 2020-06-03 /pmc/articles/PMC7269263/ /pubmed/32492034 http://dx.doi.org/10.1371/journal.pone.0233266 Text en © 2020 Tijssen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tijssen, M.
Hernlund, E.
Rhodin, M.
Bosch, S.
Voskamp, J. P.
Nielen, M.
Serra Braganςa, F. M.
Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors
title Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors
title_full Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors
title_fullStr Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors
title_full_unstemmed Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors
title_short Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors
title_sort automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7269263/
https://www.ncbi.nlm.nih.gov/pubmed/32492034
http://dx.doi.org/10.1371/journal.pone.0233266
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