Inertial Sensor-Based Analysis of Equestrian Sports Between Beginner and Professional Riders Under Different Horse Gaits

Abstract

A correct riding posture is essential for a good performance in equestrian. This paper proposes a method of equestrian analysis based on body sensor networks. First of all, raw sensor data corresponding to professional (PRO) riders and beginners (BEGs) under different horse gaits were collected by inertial sensor nodes. Then, after an initial sensor alignment with the reduced error, a kind of motion reconstruction method based on the gradient descent method was used to fuse the sensor data to constantly update the rider’s attitude information. In addition, to evaluate the tracking accuracy of joint angle, an Northern Digital optical system was deployed to verify the accuracy of our method. Using the attitude information, the motions of riders in equestrian sports were reconstructed by combining the method of human kinematics. Finally, in view of the two aspects of exercise intensity and joint angle, a quantitative analysis of key features between PRO and BEG in walking and rising trot was carried out by combining with the kinematic analysis method. Experiments conducted in this paper demonstrate that the estimation errors are well controlled, and our method can better reflect the similarities and differences between PRO and BEG in equestrian sports and provide quantitative data for equestrian coaches or riders, which can be used to improve the riding skills of riders.