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Selected Publication:

Type of publication: Journal Article
Type of document: Full Paper
Presentation type: Presentation
Invited Speaker

Year: 2009

Authors: Witte, K; Schobesberger, H; Peham, C

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

Source: 3rd European Workshop on Movement Science, Amsterdam, NETHERLANDS, Netherlands, MAY 31-JUN 02, 2007. Hum Mov Sci. 2009; 28(3):394-405

Authors Vetmeduni Vienna:

Peham Christian
Schobesberger Hermann

Vetmed Research Units
University Equine Clinic, Clinical Unit of Equine Surgery

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.

Keywords Pubmed: Animals
Biomechanical Phenomena/physiology*
Exercise Test/veterinary
Models, Biological
Physical Conditioning, Animal/methods

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