Study on Horse-Rider Interaction Based on Body Sensor Network in Competitive Equitation

Abstract

Horse-rider interaction analysis by wearable sensors is a promising tool for monitoring equestrian training. In this paper, a body sensor network (BSN) based equestrian motion analysis system is developed, which combines bespoke inertial measurement units (IMU) and MindWave electroencephalography (EEG) acquisition equipment. To fuse the mechanical and EEG signals collected from the system, emotional and attitude information can be obtained to analyze the interaction between the rider and horse in equestrian training. For motion data fusion, a novel method, exercise intensity extend kalman filter (EID-EKF), is proposed, which can also reconstruct the riders’ posture in different gaits by establishing a biomechanical model. The accuracy of our method is verified with the optical system Vicon to support the motion capture for four riding styles (walking, sitting troth, rising trot, canter). Finally, the emotion changes of the riders with different levels are quantified, and kinematic analysis is carried out by combining with inertial and emotional information. It is concluded from the experiment results that the estimation errors are well controlled, and motion patterns acquired according to the kinematic analysis are consistent with the actual situation.