Akbulut, AkhanAşçı, GüvenGüngör, FerayTarakcı, ElaAydın, Muhammed AliZaim, Abdül Halim2020-11-212020-11-2120199781730000000https://doi.org/10.1109/TIPTEKNO.2019.8895247https://hdl.handle.net/11467/41252019 Medical Technologies Congress, TIPTEKNO 2019 -- 3 October 2019 through 5 October 2019 -- -- 154293Phantom 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.eninfo:eu-repo/semantics/closedAccessDeep LearningEMGMovement RecognitionPhantom Limb PainPhantom MovementConference ObjectN/AWOS:000516830900125N/A2-s2.0-8507561770110.1109/TIPTEKNO.2019.8895247