Deep learning approaches for phantom movement recognition

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dc.contributor.authorAkbulut, Akhan
dc.contributor.authorAşçı, Güven
dc.contributor.authorGüngör, Feray
dc.contributor.authorTarakcı, Ela
dc.contributor.authorAydın, Muhammed Ali
dc.contributor.authorZaim, Abdül Halim
dc.date.accessioned2020-11-21T15:56:22Z
dc.date.available2020-11-21T15:56:22Z
dc.date.issued2019en_US
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description2019 Medical Technologies Congress, TIPTEKNO 2019 -- 3 October 2019 through 5 October 2019 -- -- 154293en_US
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. © 2019 IEEE.en_US
dc.description.sponsorshipno.EEEAG-117E579 Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÃœBITAK -- ACKNOWLEDGMENT This study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant no.EEEAG-117E579. -- --en_US
dc.identifier.doi10.1109/TIPTEKNO.2019.8895247en_US
dc.identifier.isbn9781730000000
dc.identifier.scopus2-s2.0-85075617701en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/TIPTEKNO.2019.8895247
dc.identifier.urihttps://hdl.handle.net/11467/4125
dc.identifier.wosWOS:000516830900125en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofTIPTEKNO 2019 - Tip Teknolojileri Kongresien_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectEMGen_US
dc.subjectMovement Recognitionen_US
dc.subjectPhantom Limb Painen_US
dc.subjectPhantom Movementen_US
dc.titleDeep learning approaches for phantom movement recognitionen_US
dc.typeConference Objecten_US

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