EMG based Hand Gesture Classification using Empirical Mode Decomposition Time-Series and Deep Learning

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 (IMEs) 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 101 layer Convolution Neural Network (CNN) based on Residual Networks (ResNet) architecture, which is a DL model.