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Animals : an open access journal from MDPI2021; 11(10); doi: 10.3390/ani11102904

Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data.

Abstract: Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models based on a neural network. A jumping training could be classified with a high accuracy of 100 %, while a dressage training could be classified with an accuracy of 96.29%. Assigning the dressage movements to 11, 6 or 4 superclasses results in higher accuracies of 98.87%, 99.10% and 100%, respectively. Furthermore, during dressage training, the side of movement could be identified with an accuracy of 97.08%. In addition, a velocity estimation model was developed based on the measured velocities of seven horses performing the collected, working, and extended gaits during a dressage training. For the walk, trot, and canter paces, the velocities could be estimated accurately with a low root mean square error of 0.07 m/s, 0.14 m/s, and 0.42 m/s, respectively.
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Publication Date: 2021-10-07 PubMed ID: 34679925PubMed Central: PMC8532712DOI: 10.3390/ani11102904Google 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.

The research investigates the effectiveness of using accelerometer data to detect and classify equine training activities such as horse jumping and dressage. The data collected showed a high accuracy rate, providing a potential solution for the equestrian world to track and enhance rider and horse performance.

Collecting and Using Accelerometer Data

  • The study collected leg accelerometer data from 14 well-trained horses during jumping and dressage trainings.
  • Based on this data, they developed specific models, using a neural network, to classify the activities being conducted.
  • The study highlights the ability of this methodology to distinguish different horse training activities beyond basic gaits, something the current technology struggles with.

Accuracy of Activity Classification

  • Jumping training activities were accurately classified 100% of the time, signifying the reliability of the models developed.
  • The dressage training activities were accurately classified 96.29% of the time.
  • Accuracy improved when dressage activities were grouped into larger superclass categories, with accuracies of 98.87%, 99.10%, and 100% when sorted into 11, 6, or 4 superclasses respectively.
  • The horse’s side of movement during dressage training was identified with 97.08% accuracy.
  • Overall these high percentages suggest a great potential for using this data in improving performance understanding and tracking.

Velocity Estimation Model

  • Alongside activity classification, the researchers developed a velocity estimation model.
  • This model was based on the measured velocities of seven horses performing various gaits during a dressage training.
  • For walk, trot, and canter paces, the velocities could be accurately measured with a low root mean square error.
  • The estimated velocities had errors of only 0.07 m/s, 0.14 m/s, and 0.42 m/s respectively, further adding to the potential application of these methods in trainings.

Cite This Article

APA
Eerdekens A, Deruyck M, Fontaine J, Damiaans B, Martens L, De Poorter E, Govaere J, Plets D, Joseph W. (2021). Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data. Animals (Basel), 11(10). https://doi.org/10.3390/ani11102904

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 11
Issue: 10

Researcher Affiliations

Eerdekens, Anniek
  • WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
Deruyck, Margot
  • WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
Fontaine, Jaron
  • IDLab-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
Damiaans, Bert
  • VETMED, Department of Reproduction, Obstetrics and Herd Health, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium.
Martens, Luc
  • WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
De Poorter, Eli
  • IDLab-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
Govaere, Jan
  • VETMED, Department of Reproduction, Obstetrics and Herd Health, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium.
Plets, David
  • WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
Joseph, Wout
  • WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.

Conflict of Interest Statement

The authors declare no conflict of interest.

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Citations

This article has been cited 5 times.
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