Classification of Hand Opening/Closing and Fingers by Using Two Channel Surface EMG Signal

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dc.authoridTağluk, M. Emin/0000-0001-7789-6376
dc.authoridERTUGRUL, Ömer Faruk/0000-0003-0710-0867
dc.authoridTekin, Ramazan/0000-0003-4325-6922
dc.authorwosidTağluk, M. Emin/ABH-1005-2020
dc.authorwosidERTUGRUL, Ömer Faruk/F-7057-2015
dc.authorwosidTekin, Ramazan/I-1519-2014
dc.contributor.authorSezgin, Necmettin
dc.contributor.authorErtugrul, Omer Faruk
dc.contributor.authorTekin, Ramazan
dc.contributor.authorTagluk, Mehmet Emin
dc.date.accessioned2024-08-04T20:44:14Z
dc.date.available2024-08-04T20:44:14Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEYen_US
dc.description.abstractIn this study, two-channel surface electromyogram (sEMG) signals were used to classify hand open/close with fingers. The bispectrum analysis of the sEMG signal recorded with surface electrodes near the region of the muscle bundles on the front and back of the forearm was classified by extreme learning machines (ELM) based on phase matches in the EMG signal. EMG signals belonging to 17 persons, 8 males and 9 females, with an average age of 24 were used in the study. The fingers were classified using ELM algorithm with 94.60% accuracy in average. From the information obtained through this study, it seems possible to control finger movements and hand opening/closing by using muscle activities of the forearm which we hope to lead to control of intelligent prosthesis hands with high degree of freedom.en_US
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Scien_US
dc.identifier.isbn978-1-5386-1880-6
dc.identifier.scopus2-s2.0-85039924448en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11616/98101
dc.identifier.wosWOS:000426868700008en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2017 International Artificial Intelligence and Data Processing Symposium (Idap)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSurface electromyogramen_US
dc.subjectExtreme learning machineen_US
dc.subjectBispectrum analysisen_US
dc.titleClassification of Hand Opening/Closing and Fingers by Using Two Channel Surface EMG Signalen_US
dc.typeConference Objecten_US

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