W I Schöllhorn1, C Peham, T Licka, M Scheidl. 1. Faculty for Psychology and Sport Science, University of Muenster, Horstmarer Landweg 62b, 41849 Muenster, Germany; and tUniversity of Veterinary Medicine, Vienna, Austria.
Abstract
REASONS FOR PERFORMING STUDY: Interactions of various systems were investigated in several studies of dynamic systems, but the interactions between horse and rider have not yet been documented. These interactions include the rider's ability to control the horse, adapt to the horse and maintain both participants' body position. An optimum interaction is also adapted to the individual nature of the horse. OBJECTIVE: To identify rider-horse interactions by means of artificial neural nets analysing the time-continuous pattern. METHODS: Fourteen horses were measured trotting on hand, and ridden at working trot with a professional and a recreational rider using a 3D high speed video system (120 Hz)1. Angles were calculated after low pass filtering (5-20 Hz). Horse movements were described by 2D angles, angular velocities, and angular accelerations of variables of the right body side: hind and front fetlock, head, back and the summation angle of carpus, elbow, and shoulder, the summation angle of hock, stifle, and hip. Distances between the trajectories of the feature vectors in an N = 11 x 11 Kohonen map were determined and analysed by means of a cluster analysis. RESULTS: Depending on the variables included, both rider specific as well as horse specific movement patterns could be identified. The time courses of the head angle indicate a movement pattern mainly dominated by the rider, whereas the time courses of variables of the hind fetlock and hock in most cases did not show differences between the conditions with, and without, rider. The skill of the professional rider could be documented with a higher adaptation to the horse's movement pattern. CONCLUSION AND POTENTIAL RELEVANCE: The presented time course oriented approach provides a sensitive tool in order to quantify the interaction of rider and horse.
REASONS FOR PERFORMING STUDY: Interactions of various systems were investigated in several studies of dynamic systems, but the interactions between horse and rider have not yet been documented. These interactions include the rider's ability to control the horse, adapt to the horse and maintain both participants' body position. An optimum interaction is also adapted to the individual nature of the horse. OBJECTIVE: To identify rider-horse interactions by means of artificial neural nets analysing the time-continuous pattern. METHODS: Fourteen horses were measured trotting on hand, and ridden at working trot with a professional and a recreational rider using a 3D high speed video system (120 Hz)1. Angles were calculated after low pass filtering (5-20 Hz). Horse movements were described by 2D angles, angular velocities, and angular accelerations of variables of the right body side: hind and front fetlock, head, back and the summation angle of carpus, elbow, and shoulder, the summation angle of hock, stifle, and hip. Distances between the trajectories of the feature vectors in an N = 11 x 11 Kohonen map were determined and analysed by means of a cluster analysis. RESULTS: Depending on the variables included, both rider specific as well as horse specific movement patterns could be identified. The time courses of the head angle indicate a movement pattern mainly dominated by the rider, whereas the time courses of variables of the hind fetlock and hock in most cases did not show differences between the conditions with, and without, rider. The skill of the professional rider could be documented with a higher adaptation to the horse's movement pattern. CONCLUSION AND POTENTIAL RELEVANCE: The presented time course oriented approach provides a sensitive tool in order to quantify the interaction of rider and horse.
Authors: Fernando J Marqués; Cheryl Waldner; Stephen Reed; Fernando Autet; Louise Corbeil; John Campbell Journal: Can J Vet Res Date: 2014-04 Impact factor: 1.310
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Authors: Kate Horan; Kieran Kourdache; James Coburn; Peter Day; Henry Carnall; Dan Harborne; Liam Brinkley; Lucy Hammond; Sean Millard; Bryony Lancaster; Thilo Pfau Journal: PLoS One Date: 2021-11-23 Impact factor: 3.240