Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/8061
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dc.contributor.authorPowar, O.S.
dc.contributor.authorChemmangat, K.
dc.date.accessioned2020-03-30T10:18:02Z-
dc.date.available2020-03-30T10:18:02Z-
dc.date.issued2017
dc.identifier.citationIEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017, Vol.2017-December, , pp.1022-1026en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8061-
dc.description.abstractPattern recognition scheme is used for discriminating various classes of hand motion with feature extracted from the surface electromyography signals. However, while using a relatively large feature set for classification process, the computational complexity increases tremendously. To overcome this, the paper implements feature selection technique using wrapper evaluation and four different search methods without significantly affecting the classification accuracy. The performance of the features is tested on surface electromyography data collected from seven subjects, with eight classes of movements. Practical results indicate that using feature selection methods can achieve the same accuracy with lesser number of features. � 2017 IEEE.en_US
dc.titleFeature selection for myoelectric pattern recognition using two channel surface electromyography signalsen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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