Classification of physical actions from surface EMG signals using the wavelet packet transform and local binary patterns

dc.contributor.author	Alçin, Ö.F.
dc.contributor.author	Budak, Ü.
dc.contributor.author	Aslan, M.
dc.contributor.author	Akbulut, Y.
dc.contributor.author	Cömert, Z.
dc.contributor.author	Akpinar, M.H.
dc.contributor.author	Şengür, A.
dc.date.accessioned	2021-12-16T10:11:52Z
dc.date.available	2021-12-16T10:11:52Z
dc.date.issued	2020
dc.identifier.isbn	9780750332798; 9780750332774
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/12810
dc.description.abstract	Physical action recognition is a hot topic in human-machine interactions. It has potential uses in helping disabled people and in various robotic applications. Electromyography (EMG) signals measure the electrical activity of the muscular systems involved
dc.language.iso	English
dc.publisher	Institute of Physics Publishing
dc.source	Modelling and Analysis of Active Biopotential Signals in Healthcare, Volume 1
dc.title	Classification of physical actions from surface EMG signals using the wavelet packet transform and local binary patterns
dc.type	Book Chapter
dc.identifier.endpage	Aug-31
dc.identifier.scopus	2-s2.0-85096251919

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