Convolutional neural network for early detection of lameness and irregularity in horses using an IMU sensor

Abstract

Lameness and gait irregularities are significant concerns in equine health management, affecting performance, welfare, and economic value. Traditional observational methods rely on subjective expert assessments, which can lead to inconsistencies in detecting subtle or early-stage lameness. While AI-based approaches have emerged, many require multiple sensors, force plates, or video systems, making them costly and impractical for field deployment. In this applied research study, we present a stride-level classification system that utilizes a single inertial measurement unit (IMU) and a one-dimensional convolutional neural network (1D CNN) to objectively differentiate between sound and lame horses, with a primary focus on the trot gait. The proposed system was tested under real-world conditions, achieving a 90% session-level accuracy with no false positives, demonstrating its robustness for practical applications. By employing a single, non-intrusive, and readily available sensor, our approach significantly reduces the complexity and cost of hardware requirements while maintaining high classification performance. These results highlight the potential of our CNN-based method as a field-tested, scalable solution for automated lameness detection. By enabling early diagnosis, this system offers a valuable tool for preventing minor gait irregularities from developing into severe conditions, ultimately contributing to improved equine welfare and performance in veterinary and equestrian practice.