Elicitation hybrid spatial features from HD-sEMG signals for robust classification of gestures in real-time

Recently, using high-density surface electromyography (HD-sEMG) electrodes in prosthetics have outdone the challenges of electrodes sites on the muscle, while the high accuracy of HD-sEMG signals classification will improve prosthetics performance. A new concept has emerged that the robust features extraction methods increase the efficiency of the classification regardless of the classifier. In addition, there are many factors affecting the quality of the signal, and thus the quality of classification such as stress, fatigue, disease, muscular dystrophy … etc. In this paper, these challenges will be reduced by the proposed approach for extraction hybrid features from the HD-EMG signal based on the histogram-oriented gradient (HOG) algorithm and signal intensity features, where the support vector machine (SVM) classifier is used for the classification process. The results showed high accuracy of the classification and successful in real-time tests. Also, the classification results of these experiments have overcome the challenge of long term classification.