Motion pattern analysis of gait in horseback riding by means of Principal Component Analysis

K. Witte , H. Schobesberger , C. Peham

As a consequence of the three interacting systems of horse, saddle, and rider, horseback riding is a very complex movement that is difficult to characterize by a limited number of biomechanical parameters or characteristic curves. Principal Component Analysis (PCA) is a technique for reducing multidimensional datasets to a minimal (i.e., optimally economic) set of dimensions. To apply PCA to horseback riding data, a “pattern vector” composed of the horizontal velocities of a set of body markers was determined. PCA was used to identify the major dynamic constituents of the three natural gaits of the horse: walk, trot, and canter. It was found that the trot is characterized by only one major component accounting for about 90% of the data's variance. Based on a study involving 13 horses with the same rider, additional phase plane analyses of the order parameter dynamics revealed a potential influence of the saddle type on movement coordination for the majority of horses.

Mots clés: Kinematic analysis; horseback riding; Principal Component Analysis

K. Witte, H. Schobesberger, C. Peham. Motion pattern analysis of gait in horseback riding by means of Principal Component Analysis. Human Movement Science, 2009, 28 (3), pp.394. ⟨10.1016/j.humov.2009.04.002⟩. ⟨hal-00538158⟩