Synthetically generated cow (Bos taurus) provides data for gait analysis in feedlot

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dc.contributor.authorGoldani, Ali
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorWhishaw, Ian Q.
dc.contributor.supervisorMohajerani, Majid H.
dc.date.accessioned2024-02-13T22:33:57Z
dc.date.available2024-02-13T22:33:57Z
dc.date.issued2023
dc.degree.levelMasters
dc.description.abstractAnalysis of bovine movement and behavior is crucial in detecting their motor disorders and maintaining their welfare. Quantitative gait analysis methods have been designed to facilitate this task, but the on-site subjective assessment of the gait pattern remains prominent and depends on human expertise. Gait pattern could be assessed in feedlot cattle using AI as a substitute for the absence of human diagnosis, but creating an AI diagnosis procedure requires substantial behavioral information for training the AI tool. One solution for obtaining behavioral information is to use AI-assisted tools for diagnosis based on recordings of cattle movement. In this study, we created a three-dimensional digital representation of walking cattle to generate the required information and compare its applicability to that of the actual gait patterns. We used video recordings of cattle walking and trotting, and then used them as reference to create three-dimensional pose representations. Then, we introduced variations to these representations by altering specific aspects of the original walking cow model and its environment. We then tested the combined representations against the real data to see if they can prove useful in training a deep neural network for detecting gait pattern and features. This method can compensate for the scarcity of behavioral data, provide information to create mathematical representations of specific behaviors and be used for the development of smart-phone-based diagnosis systems.
dc.identifier.urihttps://hdl.handle.net/10133/6685
dc.language.isoen
dc.proquest.subject0648
dc.proquest.subject0476
dc.proquest.subject0800
dc.proquestyesYes
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Neuroscience
dc.publisher.departmentDepartment of Neuroscience
dc.publisher.facultyArts and Science
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)
dc.subjectbovine gait analysis
dc.subjectlameness detection
dc.subjectAI gait pattern analysis
dc.subjectfeedlot cattle health
dc.subjectsynthetic data generation
dc.subjectdynamic systems theory
dc.subjectbovine movement analysis
dc.subject.lcshCattle--Locomotion--Research
dc.subject.lcshCattle--Locomotion--Mathematical models
dc.subject.lcshCattle--Locomotion--Computer simulation
dc.subject.lcshLameness in cattle--Early detection--Research
dc.subject.lcshFeedlots
dc.subject.lcshArtificial intelligence--Agricultural applications
dc.subject.lcshArtificial intelligence--Data processing
dc.subject.lcshDeep learning (Machine learning)
dc.subject.lcshDissertations, Academic
dc.titleSynthetically generated cow (Bos taurus) provides data for gait analysis in feedlot
dc.typeThesis
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