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AuthorAkbulut, Akhan
AuthorGungor, Feray
AuthorTarakci, Ela
AuthorAydin, Muhammed Ali
AuthorZaim, Abdul Halim
AuthorCatal, Cagatay
Available date2022-11-30T11:23:18Z
Publication Date2022
Publication NameComputers in Biology and Medicine
ResourceScopus
Resource2-s2.0-85139345935
URIhttp://dx.doi.org/10.1016/j.compbiomed.2022.106132
URIhttp://hdl.handle.net/10576/36772
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. 2022 The Author(s)
SponsorThis study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant no. EEEAG-117E579. The data that support the findings of this study are available on request from the principle investigator of the project EEEAG-117E579, Akhan Akbulut, PhD. The data are not publicly available due to the confidential information that could compromise the privacy of research participants. Open Access funding provided by the Qatar National Library.
Languageen
PublisherElsevier
SubjectClassification; Ensemble learning; Phantom motor execution
TitleIdentification of phantom movements with an ensemble learning approach
TypeArticle
Volume Number150
dc.accessType Open Access


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