Open AccessA Control System of SEMG Signal Based on Deep Learning
Publication year - 2020
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2020.14.52
Subject(s) - computer science , artificial intelligence , signal (programming language) , field (mathematics) , convolutional neural network , artificial neural network , control system , pattern recognition (psychology) , scheme (mathematics) , deep learning , signal processing , control engineering , engineering , digital signal processing , computer hardware , mathematical analysis , mathematics , pure mathematics , electrical engineering , programming language
The research of control system based on sEMG signal is a popular field at present. It collects bioelectricity of human body through surface electrode. It has the new characteristic of subject fusion, and it is the combination of engineering technology and medical theory, specifically the application of cross combination of control science and electrophysiology. In this paper, the human surface EMG signal is taken as the research object, and a manipulator control system based on one-dimensional convolutional neural network (CNN) is proposed, and the functions and implementation methods of each part of the system are analyzed. The experimental results show that the recognition accuracy of the training model is 0.973, and the design scheme of EMG signal recognition and classification system with deep learning method is feasible. The successful design of the system provides technical support and theoretical basis for the further study of electrophysiological signals.