TY - JOUR
T1 - Detecting fatigue of sport horses with biomechanical gait features using inertial sensors
AU - Darbandi, Hamed
AU - Munsters, Carolien
AU - Parmentier, J.I.M.
AU - Havinga, Paul J.M.
N1 - Funding Information:
This study was partly funded by EFRO OP-Oost (project Paardensprong - Grant number: OP-2014-2023-Oost-PROJ-00841”). For the remaining part, the authors were funded by their respective organizations. There was no additional external funding received for this study. The authors wish to thank Esther Siegers from Utrecht University, and all the riders, veterinarians, and animal caretakers that helped with the data collection.
Publisher Copyright:
© 2023 Darbandi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Financial transaction number:
2500064780
PY - 2023/4/14
Y1 - 2023/4/14
N2 - Detection of fatigue helps prevent injuries and optimize the performance of horses. Previous studies tried to determine fatigue using physiological parameters. However, measuring the physiological parameters, e.g., plasma lactate, is invasive and can be affected by different factors. In addition, the measurement cannot be done automatically and requires a veterinarian for sample collection. This study investigated the possibility of detecting fatigue non-invasively using a minimum number of body-mounted inertial sensors. Using the inertial sensors, sixty sport horses were measured during walk and trot before and after high and low-intensity exercises. Then, biomechanical features were extracted from the output signals. A number of features were assigned as important fatigue indicators using neighborhood component analysis. Based on the fatigue indicators, machine learning models were developed for classifying strides to non-fatigue and fatigue. As an outcome, this study confirmed that biomechanical features can indicate fatigue in horses, such as stance duration, swing duration, and limb range of motion. The fatigue classification model resulted in high accuracy during both walk and trot. In conclusion, fatigue can be detected during exercise by using the output of body-mounted inertial sensors.
AB - Detection of fatigue helps prevent injuries and optimize the performance of horses. Previous studies tried to determine fatigue using physiological parameters. However, measuring the physiological parameters, e.g., plasma lactate, is invasive and can be affected by different factors. In addition, the measurement cannot be done automatically and requires a veterinarian for sample collection. This study investigated the possibility of detecting fatigue non-invasively using a minimum number of body-mounted inertial sensors. Using the inertial sensors, sixty sport horses were measured during walk and trot before and after high and low-intensity exercises. Then, biomechanical features were extracted from the output signals. A number of features were assigned as important fatigue indicators using neighborhood component analysis. Based on the fatigue indicators, machine learning models were developed for classifying strides to non-fatigue and fatigue. As an outcome, this study confirmed that biomechanical features can indicate fatigue in horses, such as stance duration, swing duration, and limb range of motion. The fatigue classification model resulted in high accuracy during both walk and trot. In conclusion, fatigue can be detected during exercise by using the output of body-mounted inertial sensors.
U2 - 10.1371/journal.pone.0284554
DO - 10.1371/journal.pone.0284554
M3 - Article
SN - 1932-6203
VL - 18
JO - PLoS ONE
JF - PLoS ONE
IS - 4
M1 - e0284554
ER -