Browsing by Author "Kisa D.H."

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Citation - WoS: 12
Citation - Scopus: 17
Emg Based Hand Gesture Classification Using Empirical Mode Decomposition Time-Series and Deep Learning
(Institute of Electrical and Electronics Engineers Inc., 2020) Kisa D.H.; Ozdemir M.A.; Guren O.; Akan A.; Ozdemir, Mehmet Akif; Kisa, Deniz Hande; Guren, Onan; Akan, Aydin
Computer systems working with artificial intelligence can recognize movements and gestures to be used for many purposes. In order to perform recognition, the electrical activity of the muscles can be utilized which is represented by electromyography (EMG) and EMG is not a stationary biological signal. EMG based movement recognition systems have an important place in distinct areas like in human-computer interactions, virtual reality, prosthesis, and hand exoskeletons. In this study, a new approach based on deep learning (DL) and Empirical Mode Decomposition (EMD) is proposed to improve the accuracy rate for recognition of hand movements in its application areas. Firstly, 4-channel surface EMG (sEMG) signals were measured while simulating 7 different hand gestures, which are extension, flexion, ulnar deviation, radial deviation, punch, open hand, and rest, from 30 subjects. After that, noiseless signals were procured utilizing filters as a result of preprocessing. Then, pre-processed signals were subjected to segmentation. Thereafter, the EMD process was applied to each segmented signal and Intrinsic Mode Functions (IMFs) were obtained. The IMFs time-series which are some kind of screen images of the first 3 IMFs have been recorded. For classification, IMFs images have given as inputs and have trained to the 101layer Convolution Neural Network (CNN) based on Residual Networks (ResNet) architecture, which is a DL model. © 2020 IEEE.
Citation - WoS: 38
Citation - Scopus: 54
Emg Based Hand Gesture Recognition Using Deep Learning
(Institute of Electrical and Electronics Engineers Inc., 2020) Ozdemir M.A.; Kisa D.H.; Guren O.; Onan A.; Akan A.; Ozdemir, Mehmet Akif; Kisa, Deniz Hande; Guren, Onan; Onan, Aytug; Akan, Aydin
The 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.