EMG pattern classification based on AR modeling

dc.contributor	Graduate Program in Biomedical Engineering.
dc.contributor.advisor	Sankur, Bülent.
dc.contributor.author	Erim, Zeynep.
dc.date.accessioned	2023-03-16T13:12:04Z
dc.date.available	2023-03-16T13:12:04Z
dc.date.issued	1986.
dc.description.abstract	Myoelectric control of powered prostheses is a field of rehabilitation engineering that has received wide attention in the recent decades. In this thesis a historical perspective of the studies in the field is given. The physiological properties of muscles are reviewed. The linear models, algorithms for identifying model parameters, and basic pattern recognition considerations are outlined. A scheme to extract motion information from a single surface EMG channel is discussed. The results obtained in performance tests are given. Suggestions for future research topics are made. Major computer programs used are given in the appendix.
dc.format.extent	30cm.
dc.format.pages	ix, 74 leaves;
dc.identifier.other	BM 1986 E75
dc.identifier.uri	https://hdl.handle.net/20.500.14908/18743
dc.publisher	Thesis (M.S.)-Bogazici University. Institute of Biomedical Engineering, 1986.
dc.relation	Includes appendices.
dc.relation	Includes appendices.
dc.subject.lcsh	Electromyography.
dc.subject.lcsh	Pattern perception.
dc.title	EMG pattern classification based on AR modeling

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