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Home / Equine Research Bank / PLoS One / Detecting fatigue of sport horses with biomechanical gait fe...
PloS one2023; 18(4); e0284554; doi: 10.1371/journal.pone.0284554

Detecting fatigue of sport horses with biomechanical gait features using inertial sensors.

Abstract: 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.
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.
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Publication Date: 2023-04-14 PubMed ID: 37058516PubMed Central: PMC10104328DOI: 10.1371/journal.pone.0284554Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research focuses on using body-mounted inertial sensors on sport horses to detect fatigue non-invasively. By observing changes in certain biomechanical features, such as swing and stance duration, and limb range of motion, the study found a high accuracy in predicting fatigue using machine learning models.

Objective and Background

  • The main objective of this study was to explore non-invasive fatigue detection technologies for sport horses, particularly utilizing minimum body-mounted inertial sensors. The importance of fatigue detection lies in its role in injury prevention and performance optimization.
  • Existing fatigue detection methods largely center on invasive physiological measures which require specialized assistance, frequently a veterinarian, making them less convenient and adaptable in real-world settings. These traditional methods, such as plasma lactate measurement, can also be influenced by various factors thus potentially affecting their accuracy.

Methodology

  • Sixty sport horses were assessed in a controlled setting, with the horses carrying out both low and high-intensity exercises.
  • Before and after these exercises, the inertial sensors were used to gather data on the horses’ walk and trot.
  • Crucial biomechanical features like limb range of motion, stance period, and swing duration were mined from the output signals of these sensors.
  • The neighborhood component analysis was applied to identify the most significant fatigue indicators from the biomechanical features identified.
  • A machine learning model was then built to classify strides into non-fatigue and fatigue, based on the indicators.

Results and Conclusion

  • The study affirmed the potential of biomechanical features to detect fatigue in sporting horses. Key fatigue indicating features identified included stance duration, swing duration, and limb range of motion.
  • A machine learning model was built next that resulted in high accuracy in classifying strides into non-fatigue and fatigue, in both walk and trot states.
  • The result of this study emphasizes the viability of a non-invasive, efficient, and automatic method for detecting fatigue in horses in real-time, using body-mounted inertial sensors.

Cite This Article

APA
Darbandi H, Munsters C, Parmentier J, Havinga P. (2023). Detecting fatigue of sport horses with biomechanical gait features using inertial sensors. PLoS One, 18(4), e0284554. https://doi.org/10.1371/journal.pone.0284554

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 18
Issue: 4
Pages: e0284554
PII: e0284554

Researcher Affiliations

Darbandi, Hamed
  • Department of Computer Science, Pervasive Systems Group, University of Twente, Enschede, The Netherlands.
Munsters, Carolien
  • Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
  • Equine Integration, Hoogeloon, The Netherlands.
Parmentier, Jeanne
  • Department of Computer Science, Pervasive Systems Group, University of Twente, Enschede, The Netherlands.
  • Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Havinga, Paul
  • Department of Computer Science, Pervasive Systems Group, University of Twente, Enschede, The Netherlands.

MeSH Terms

  • Horses
  • Animals
  • Gait / physiology
  • Walking / physiology
  • Extremities
  • Machine Learning
  • Biomechanical Phenomena

Conflict of Interest Statement

The authors have declared that no competing interests exist.

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