Motion pattern analysis of gait in horseback riding by means of Principal Component Analysis.
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.
Witte
K
K
Department of Sports Science, Otto-von-Guericke-University Magdeburg, Brandenburger Str. 9, Magdeburg 39104, Germany. kerstin.witte@gse-w.uni-magdeburg.de
Schobesberger
H
H
Peham
C
C
D000818:Animals; D001696:Biomechanical Phenomena / Q000502:physiology*; D005080:Exercise Test / Q000662:veterinary; D005684:Gait / Q000502:physiology*; D006736:Horses / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D009068:Movement / Q000502:physiology; D010805:Physical Conditioning, Animal / Q000379:methods; D011187:Posture; D011312:Pressure; D013177:Sports; D016474:Weight-Bearing / Q000502:physiology*