Detecting Equine Gaits Through Rider-Worn Accelerometers

Abstract

Automatic horse gait classification offers insights into training intensity, but direct sensor attachment to horses raises concerns about discomfort, behavioral disruption, and entanglement risks. To address this, our study leverages rider-centric accelerometers for movement classification. The position of a sensor, sampling frequency, and window size of segmented signal data have a major impact on classification accuracy in activity recognition. Yet, there are no studies that have evaluated the effect of all these factors simultaneously using accelerometer data from four distinct rider locations (the knee, backbone, chest, and arm) across five riders and seven horses performing three gaits. A total of eight models were compared, and an LSTM-convolutional network (ConvLSTM2D) achieved the highest accuracy, with an average accuracy of 89.72% considering four movements (halt, walk, trot, and canter). The model performed best with an interval width of four seconds and a sampling frequency of 25 Hz. Additionally, an F1-score of 86.18% was achieved and validated using LOSOCV (Leave One Subject Out Cross-Validation).