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dc.contributor.authorKaijima, Makiko
dc.date.accessioned2014-03-03T23:14:19Z
dc.date.available2014-03-03T23:14:19Z
dc.date.issued2005-05
dc.identifier.otherkaijima_makiko_200505_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/kaijima_makiko_200505_ms
dc.identifier.urihttp://hdl.handle.net/10724/22416
dc.description.abstractArtificial neural networks (ANNs) were developed to map ground reaction force (GRF) data to subjective diagnostic scores of lameness. Twenty-one clinically normal dogs (19–32.2 kg) underwent surgery inducing osteoarthritis in the left hind stifle joint. Lameness scores were assigned by a veterinarian and GRF data were collected twice prior to and five times after the surgery. The study discussed herein focused on identifying the preferred ANN architecture and input variables extracted from GRF curves. The data were partitioned to allow the accuracy of the resulting models to be evaluated with dogs not included in model development. The results indicate that backpropagation neural networks are preferable to probabilistic neural networks. Input variables were identified in this study that capture a dog’s attempt to remove weight from an injured limb. ANNs differentiated the three classes of lameness with an accuracy ranging from 87.8–100%.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectCanine
dc.subjectDog
dc.subjectGait Analysis
dc.subjectArtificial Neural Network,Ground Reaction Force
dc.subjectDiagnosis
dc.subjectBiomechanics
dc.subjectForce Plate
dc.subjectLameness
dc.subjectProbabilistic Neural Network
dc.subjectBackpropagation
dc.subjectDecision Support
dc.titleCanine gait analysis and diagnosis using artificial neural networks and ground reaction force
dc.typeThesis
dc.description.degreeMS
dc.description.departmentArtificial Intelligence
dc.description.majorArtificial Intelligence
dc.description.advisorRonald W. McClendon
dc.description.committeeRonald W. McClendon
dc.description.committeeTimothy L. Foutz
dc.description.committeeWalter D. Potter


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