Detecting Equine Gaits Through Rider-Worn Accelerometers
Automatic horse gait classification offers insights into training intensity, but direct<br>sensor attachment to horses raises concerns about discomfort, behavioral disruption, and<br>entanglement risks. To address this, our study leverages rider-centric accelerometers for<br>moveme...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Animals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-2615/15/8/1080 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849712325182881792 |
|---|---|
| author | Jorn Schampheleer Anniek Eerdekens Wout Joseph Luc Martens Margot Deruyck |
| author_facet | Jorn Schampheleer Anniek Eerdekens Wout Joseph Luc Martens Margot Deruyck |
| author_sort | Jorn Schampheleer |
| collection | DOAJ |
| description | Automatic horse gait classification offers insights into training intensity, but direct<br>sensor attachment to horses raises concerns about discomfort, behavioral disruption, and<br>entanglement risks. To address this, our study leverages rider-centric accelerometers for<br>movement classification. The position of a sensor, sampling frequency, and window size of<br>segmented signal data have a major impact on classification accuracy in activity recognition.<br>Yet, there are no studies that have evaluated the effect of all these factors simultaneously<br>using accelerometer data from four distinct rider locations (the knee, backbone, chest, and<br>arm) across five riders and seven horses performing three gaits. A total of eight models<br>were compared, and an LSTM-convolutional network (ConvLSTM2D) achieved the highest<br>accuracy, with an average accuracy of 89.72% considering four movements (halt, walk,<br>trot, and canter). The model performed best with an interval width of four seconds and<br>a sampling frequency of 25 Hz. Additionally, an F1-score of 86.18% was achieved and<br>validated using LOSOCV (Leave One Subject Out Cross-Validation). |
| format | Article |
| id | doaj-art-1954d0d154ab4e3390708ec73d737baa |
| institution | DOAJ |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj-art-1954d0d154ab4e3390708ec73d737baa2025-08-20T03:14:19ZengMDPI AGAnimals2076-26152025-04-01158108010.3390/ani15081080Detecting Equine Gaits Through Rider-Worn AccelerometersJorn Schampheleer0Anniek Eerdekens1Wout Joseph2Luc Martens3Margot Deruyck4WAVES, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, BelgiumWAVES, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, BelgiumWAVES, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, BelgiumWAVES, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, BelgiumWAVES, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, BelgiumAutomatic horse gait classification offers insights into training intensity, but direct<br>sensor attachment to horses raises concerns about discomfort, behavioral disruption, and<br>entanglement risks. To address this, our study leverages rider-centric accelerometers for<br>movement classification. The position of a sensor, sampling frequency, and window size of<br>segmented signal data have a major impact on classification accuracy in activity recognition.<br>Yet, there are no studies that have evaluated the effect of all these factors simultaneously<br>using accelerometer data from four distinct rider locations (the knee, backbone, chest, and<br>arm) across five riders and seven horses performing three gaits. A total of eight models<br>were compared, and an LSTM-convolutional network (ConvLSTM2D) achieved the highest<br>accuracy, with an average accuracy of 89.72% considering four movements (halt, walk,<br>trot, and canter). The model performed best with an interval width of four seconds and<br>a sampling frequency of 25 Hz. Additionally, an F1-score of 86.18% was achieved and<br>validated using LOSOCV (Leave One Subject Out Cross-Validation).https://www.mdpi.com/2076-2615/15/8/1080equinesaccelerometer sensoranimal activity recognitionmachine learningconvolutional LSTM network |
| spellingShingle | Jorn Schampheleer Anniek Eerdekens Wout Joseph Luc Martens Margot Deruyck Detecting Equine Gaits Through Rider-Worn Accelerometers Animals equines accelerometer sensor animal activity recognition machine learning convolutional LSTM network |
| title | Detecting Equine Gaits Through Rider-Worn Accelerometers |
| title_full | Detecting Equine Gaits Through Rider-Worn Accelerometers |
| title_fullStr | Detecting Equine Gaits Through Rider-Worn Accelerometers |
| title_full_unstemmed | Detecting Equine Gaits Through Rider-Worn Accelerometers |
| title_short | Detecting Equine Gaits Through Rider-Worn Accelerometers |
| title_sort | detecting equine gaits through rider worn accelerometers |
| topic | equines accelerometer sensor animal activity recognition machine learning convolutional LSTM network |
| url | https://www.mdpi.com/2076-2615/15/8/1080 |
| work_keys_str_mv | AT jornschampheleer detectingequinegaitsthroughriderwornaccelerometers AT anniekeerdekens detectingequinegaitsthroughriderwornaccelerometers AT woutjoseph detectingequinegaitsthroughriderwornaccelerometers AT lucmartens detectingequinegaitsthroughriderwornaccelerometers AT margotderuyck detectingequinegaitsthroughriderwornaccelerometers |