Amputee Electromyography Signal Classification Using Convolutional Neural Network

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dc.authorid0000-0003-4236-3646en_US
dc.contributor.authorOnay, F.
dc.contributor.authorMert, Ahmet
dc.date.accessioned2021-03-20T20:26:50Z
dc.date.available2021-03-20T20:26:50Z
dc.date.issued2020
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümüen_US
dc.description2020 Medical Technologies Congress, TIPTEKNO 2020 -- 19 November 2020 through 20 November 2020 -- -- 166140en_US
dc.description.abstractThe classification of EMG signals for the amputees is important to develop a powered-prosthetic that is capable of replacing with lost limbs. The EMG signals collected from residual limbs reduce the classification accuracy due to muscle movements that cannot be realized properly. In this study, classification performance is aimed to be increased by combining CNN with root mean square (RMS) and waveform length (WL) that are used in analysis of EMG signals successfully. The features such as RMS and WL extracted from EMG signals for the classification of six hand movements at the low, medium, and high force levels were applied to CNN input, and classification results were compared with nearest neighbour and linear discriminant analysis. © 2020 IEEE.en_US
dc.identifier.doi10.1109/TIPTEKNO50054.2020.9299236en_US
dc.identifier.isbn9781728180731
dc.identifier.scopus2-s2.0-85099463427en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttp://doi.org/10.1109/TIPTEKNO50054.2020.9299236
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1282
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMert, Ahmet
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofTIPTEKNO 2020 - Tip Teknolojileri Kongresi - 2020 Medical Technologies Congress, TIPTEKNO 2020en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAmputeeen_US
dc.subjectConvolutional neural networken_US
dc.subjectElectromyographyen_US
dc.subjectPattern recognitionen_US
dc.titleAmputee Electromyography Signal Classification Using Convolutional Neural Networken_US
dc.title.alternativeAmpute Elektromiyografi Sinyallerinin Evrisimli Sinir Aglari Kullanilarak Siniflandirilmasien_US
dc.typeConference Objecten_US

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