Abstract
ABSTRACT 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Australian Journal of Electrical and Electronics Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
