Identification of phantom movements with an ensemble learning approach

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dc.contributor.authorAkbulut, Akhan
dc.contributor.authorGungor, Feray
dc.contributor.authorTarakci, Ela
dc.contributor.authorAydin, Muhammed Ali
dc.contributor.authorZaim, Abdul Halim
dc.contributor.authorCatal, Cagatay
dc.date.accessioned2023-01-23T11:29:26Z
dc.date.available2023-01-23T11:29:26Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractPhantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition of these movements is crucial for both technology-based amputee rehabilitation and prosthetic control. The aim of the current study is to classify and recognize the phantom movements in four different amputation levels of the upper and lower extremities. In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. In this context, sEMG signals obtained from 38 amputees and 25 healthy individuals were collected and the dataset was created. Studies of processing sEMG signals in amputees are rather limited, and studies are generally on the classification of upper extremity and hand movements. Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. The accuracy of the movement pattern recognition in healthy people was up to 96.33%, this was at most 79.16% in amputees.en_US
dc.identifier.doi10.1016/j.compbiomed.2022.106132en_US
dc.identifier.pmid36195047en_US
dc.identifier.scopus2-s2.0-85139345935en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6143
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2022.106132
dc.identifier.volume150en_US
dc.identifier.wosWOS:000878510400006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClassification; Ensemble learning; Phantom motor executionen_US
dc.titleIdentification of phantom movements with an ensemble learning approachen_US
dc.typeArticleen_US

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