Analysis of the Motion Postures in Equestrian Sports Based on Multi-Sensor Data Fusion

Abstract

Horse riding, in its essence, is both an art and a captivating sport. In these events, riders showcase their refined skills and deep bond with their horses, most notably through changes in physical positioning. This research aims to accurately capture and analyze the spatial postures of riders in equestrian sports, exploring the dynamic differences between professional and amateur riders. To achieve this, we employed a multi-sensor data fusion approach based on human kinetics theories, enhanced by an extended Kalman filter. This method, combined with an optical tracking system, enabled us to intricately compare and analyze the 3D postures of riders during walking and trotting phases. Our research captured these nuanced postural shifts using an inertial sensor network, yielding nine-axis motion data of riders during their performance. The accuracy of our fusion technique was validated using the optical tracking system. By analyzing the motion data, we discerned posture variations among the riders of differing expertise levels, even when executing the same riding technique. The insights from this research offer a quantifiable metric for refining equestrian training and contribute to understanding the complex dynamics of rider–horse interaction.