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Gait analysis of hoof strain data.

Bajcar, Ellen.

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Gait analysis of hoof strain data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Gait analysis of hoof strain data./
作者:	Bajcar, Ellen.
面頁冊數:	173 p.
附註:	Source: Masters Abstracts International, Volume: 42-06, page: 2224.
Contained By:	Masters Abstracts International42-06.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ90631
ISBN:	0612906310

Gait analysis of hoof strain data.
Bajcar, Ellen.

Gait analysis of hoof strain data. - 173 p.

Source: Masters Abstracts International, Volume: 42-06, page: 2224.

Thesis (M.Sc.)--University of Guelph (Canada), 2004.

The high value of competition horses and the risk of injury involved in sports drive the dependency on efficient veterinary support. Detection and evaluation of lameness by subjective visual assessment is a difficult medical task. The examiner needs to consider several different and rapidly changing body movement patterns. When the patterns become too complex, tools like Artificial Neural Network (ANN) can be useful. ANN can be gainfully applied to gait analysis, if the system has the ability to distinguish stride characteristics and to differentiate pathological gait.

ISBN: 0612906310Subjects--Topical Terms:

626642
Computer Science.

Gait analysis of hoof strain data.
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100 1 $a Bajcar, Ellen. $3 1935070
245 1 0 $a Gait analysis of hoof strain data.
300 $a 173 p.
500 $a Source: Masters Abstracts International, Volume: 42-06, page: 2224.
500 $a Adviser: David Calvert.
502 $a Thesis (M.Sc.)--University of Guelph (Canada), 2004.
520 $a The high value of competition horses and the risk of injury involved in sports drive the dependency on efficient veterinary support. Detection and evaluation of lameness by subjective visual assessment is a difficult medical task. The examiner needs to consider several different and rapidly changing body movement patterns. When the patterns become too complex, tools like Artificial Neural Network (ANN) can be useful. ANN can be gainfully applied to gait analysis, if the system has the ability to distinguish stride characteristics and to differentiate pathological gait.
520 $a A Self-organizing Map (SOM) and Radial Basis Functions (RBF) network was trained to cluster and classify strain measurement data collected from a single hoof of moving horses. The performance of the network was examined by presenting different combinations of the available data and by varying network parameters. The method was successful in differentiating certain stride characteristics of individual horses, such as gait fingerprint, shoeing, gait, speed, and direction of movement. The neural network is still in search of the lame horse. The network's ability to distinguish the various stride characteristics encourages further investigation.
590 $a School code: 0081.
650 4 $a Computer Science. $3 626642
650 4 $a Biology, Veterinary Science. $3 1021733
690 $a 0984
690 $a 0778
710 2 0 $a University of Guelph (Canada). $3 1018650
773 0 $t Masters Abstracts International $g 42-06.
790 1 0 $a Calvert, David, $e advisor
790 $a 0081
791 $a M.Sc.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ90631