Classification of EMG signals taken from arm with hybrid CNN-SVM architecture

Abstract

Analysis based on the classification of electromyography (EMG) signals, the bioelectrical signs that appear during the contraction of the muscles, can be used in many prosthetic control applications. For this purpose, the classification of the EMG signal is considered a pattern-recognition problem that can be used to improve the functionality and ease of control of reinforced upper-limb prostheses. Four different EMG-signal patterns taken from the biceps and triceps muscles were analyzed via hybrid deep-learning methods after spectrogram-based preprocessing. Four hundred EMG spectrograms obtained by preprocessing were classified with hybrid deep-learning techniques based on AlexNet, GoogLeNet, and ResNet18. The classification was conducted using a support vector machine instead of the classification layers after the pooling layer of deep-learning architectures used in the hybrid system. In general, acceptable classification results were achieved with all techniques used, and the highest performance was obtained with the hybrid system created with AlexNet architecture. The hybrid-classification achievements with AlexNet, GoogLeNet, and Resnet18 were 99.17%, 95.83%, and 93.33%, respectively. These results show that the proposed architectures can be used in prosthetic controls created using EMG signals.