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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Animals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-2615/15/8/1080 |
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| Summary: | 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). |
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| ISSN: | 2076-2615 |