Identifying patterns in trunk/head/elbow changes of riders and non-riders: A cluster analysis approach.

Correct rider oscillation and position are the basics for a good horseback riding performance. In this paper, we propose a framework for the automatic analysis of athletes behaviour based on cluster analysis. Two groups of athletes (riders vs non-riders) were assigned to a horseback riding simulator exercise. The participants exercised four different incremental horse oscillation frequencies. This paper studies the postural coordination, by computing the different discrete relative phases of head-horse, elbow-horse and trunk-horse oscillations. Two clustering algorithms are then applied to automatically identify the change of rider and non-rider behaviour in terms of postural coordination. The results showed that the postural coordination was influenced by the level of rider expertise. More diverse behaviour was observed for non-riders. At the opposite, riders produced lower postural displacements and deployed more efficient postural control. The postural coordination for both groups was also influenced by the oscillation frequencies.