Title: Hand motions recognition based on sEMG nonlinear feature and time domain feature fusion

Authors: Jiahan Li; Gongfa Li; Ying Sun; Guozhang Jiang; Bo Tao; Shuang Xu

Addresses: Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Hubei, Wuhan, 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, and Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, 430081, China; Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Hubei, Wuhan, 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, and Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, 430081, China; Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Hubei, Wuhan, 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, and Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, 430081, China; Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Hubei, Wuhan, 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, and Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, 430081, China; Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Hubei, Wuhan, 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, and Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, 430081, China; Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Hubei, Wuhan, 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, and Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, 430081, China; Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, 430081, China

Abstract: In recent years, the development of many rehabilitation robots, bionic prostheses and other sports rehabilitation equipment, which are used to assist the body to restore body movement function, has been paid more and more attention. The classification framework of this paper is a pattern recognition framework. The feature extraction of sEMG is to extract the physical quantity or a set of physical features that fully represent the characteristics of the action class from the electromyogram corresponding to the action of the human hand, in order to distinguish the other types of motion. It is very important step in hand movement recognition. In this paper, the newly developed sEMG nonlinear features AMR are fused with the traditional sEMG time-domain features WL. Feature fusion using SVM-DS fusion algorithm. Hand motions recognition based on feature fusion is improved in accuracy and stability. The accuracy of recognition can be stabilised over 95%.

Keywords: pattern recognition; feature extraction; SVM; D-S evidence theory; feature fusion; sEMG.

DOI: 10.1504/IJICA.2019.100510

International Journal of Innovative Computing and Applications, 2019 Vol.10 No.1, pp.43 - 50

Received: 01 Aug 2018
Accepted: 28 Aug 2018
Published online: 29 Jun 2019 *

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