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dc.contributor.authorAkbulut, Akhan
dc.contributor.authorAşçı, Güven
dc.contributor.authorGüngör, Faray
dc.contributor.authorTarakçı, Ela
dc.contributor.authorAydın, Muhammed Ali
dc.contributor.authorZaim, Abdül Halim
dc.date.accessioned2019-10-25T13:21:37Z
dc.date.available2019-10-25T13:21:37Z
dc.date.issued2019-11-03
dc.identifier.urihttps://hdl.handle.net/11413/5486
dc.description.abstractPhantom limb pain has a negative effect on the life of individuals as a frequent consequence of limb amputation. The movement ability on the lost extremity can still be maintained after the amputation or deafferentation, which is called the phantom movement. The detection of these movements makes sense for cybertherapy and prosthetic control for amputees. In this paper, we employed several deep learning approaches to recognize phantom movements of the three different amputation regions including above-elbow, below-knee and above-knee. We created a dataset that contains 25 healthy and 16 amputee participants’ surface electromyography (sEMG) readings via a wearable device with 2-channel EMG sensors. We compared the results of three different deep learning methods, respec­tively, Multilayer Perceptron, Convolutional Neural Network, and Recurrent Neural Network with the accuracies of two well-known shallow methods, k Nearest Neighbor and Random Forest. Our experiments indicate, Convolutional Neural Network-based model achieved an accuracy of 74.48% in recognizing phantom movements of amputees.
dc.language.isoen_UStr_TR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectPhantom Limb Pain
dc.subjectPhantom Movement
dc.subjectDeep Learning
dc.subjectEMG
dc.subjectMovement Recognition
dc.titleDeep Learning Approaches for Phantom Movement Recognition
dc.typeconferenceObjecttr_TR
dc.contributor.authorID116056tr_TR
dc.contributor.authorID285689tr_TR
dc.contributor.authorID277179tr_TR
dc.contributor.authorID101760tr_TR
dc.contributor.authorID176402tr_TR
dc.contributor.authorID8693tr_TR
dc.relation.journalTıp Teknolojileri Kongresi, TIPTEKNO'19tr_TR
dc.identifier.scopus2-s2.0-85075617701


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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States