Chen, X.; Li, F.Z.; Li, J.X.; Fan, Q.J.; Kwan, P.; Cao, K.T.; Zheng, W.X.; Guo, L.F. Real-Time Horse Behavior Classification Based on Wearable Devices and Multi-level Threshold Analysis. Preprints2024, 2024012046. https://doi.org/10.20944/preprints202401.2046.v1
APA Style
Chen, X., Li, F.Z., Li, J.X., Fan, Q.J., Kwan, P., Cao, K.T., Zheng, W.X., & Guo, L.F. (2024). Real-Time Horse Behavior Classification Based on Wearable Devices and Multi-level Threshold Analysis. Preprints. https://doi.org/10.20944/preprints202401.2046.v1
Chicago/Turabian Style
Chen, X., Wen xin Zheng and Lei feng Guo. 2024 "Real-Time Horse Behavior Classification Based on Wearable Devices and Multi-level Threshold Analysis" Preprints. https://doi.org/10.20944/preprints202401.2046.v1
Abstract
Analyzing horse behavior is crucial for assessing training quality, particularly in accurately identifying actions like standing, walking, and running. Research has been conducted both domestically and internationally in this regard; however, challenges persist, including reliance on single sensors, poor real-time performance, and low identification accuracy. In view of these challenges, this study investigated real-time identification of horse behavior based on wearable devices. The system, centered around a microcontroller and utilizing a 4G network as a carrier, employs multi-axis IMU sensors as input sources to perceive horse posture. The study proposed a behavior classification method based on analysis of acceleration thresholds. The method consists primarily of two sequential stages: first, the resultant acceleration interval counting method, which employs a nonlinear segmentation approach for initial behavior classification; and second, the statistical analysis of variance parameters between segments, which when coupled with multi-level threshold processing, achieves a refined classification. Experimental results indicated that the interval counting method alone achieved an accuracy of 87.55%, while for the variance analysis method alone the accuracy was 90.87%. The proposed method, comprising the two stages, reached a classification accuracy of 91.57%, underpinning its usefulness in supporting future equine research.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
