Ozdemir M.A.Kisa D.H.Guren O.Onan A.Akan A.2023-06-162023-06-1620209.78E+12https://doi.org/10.1109/TIPTEKNO50054.2020.9299264https://hdl.handle.net/20.500.14365/36412020 Medical Technologies Congress, TIPTEKNO 2020 -- 19 November 2020 through 20 November 2020 -- 166140The Electromyography (EMG) signal is a nonstationary bio-signal based on the measurement of the electrical activity of the muscles. EMG based recognition systems play an important role in many fields such as diagnosis of neuromuscular diseases, human-computer interactions, console games, sign language detection, virtual reality applications, and amputee device controls. In this study, a novel approach based on deep learning has been proposed to improve the accuracy rate in the prediction of hand movements. Firstly, 4-channel surface EMG (sEMG) signals have been measured while simulating 7 different hand gestures (Extension, Flexion, Open Hand, Punch, Radial Deviation, Rest, and Ulnar Deviation) from 30 participants. The obtained sEMG signals have been segmented into sections where each movement was found. Then, spectrogram images of the segmented sEMG signals have been created by means of ShortTime Fourier Transform (STFT). The created colored spectrogram images have trained with 50-layer Convolutional Neural Network (CNN) based on Residual Networks (ResNet) architecture. Owing to the proposed method, test accuracy of 99.59% and F1 Score of 99.57% have achieved for 7 different hand gesture classifications. © 2020 IEEE.eninfo:eu-repo/semantics/closedAccessCNNDeep LearningEMGHand GestureResNetSpectrogram STFTBiomedical engineeringBiomedical signal processingComputer gamesConvolutional neural networksDiagnosisGesture recognitionHuman computer interactionNeurophysiologySpectrographsAccuracy rateDevice controlElectrical activitiesHand-gesture recognitionNeuromuscular diseaseNonstationaryRecognition systemsShort time Fourier transformsDeep learningConference Object10.1109/TIPTEKNO50054.2020.92992642-s2.0-85099461588