Hand motions recognition based on sEMG nonlinear feature and time domain feature fusion. (28th June 2019)

• Record Type: Journal Article
• Authors: Li, Jiahan; Li, Gongfa; Sun, Ying; Jiang, Guozhang; Tao, Bo; Xu, Shuang
• Is Part Of: International journal of innovative computing and applications. Volume 10:Number 1(2019)
• Page Start: 43
• Page End: 50
• Publication Date: 2019-06-28
• Subjects: pattern recognition -- feature extraction -- SVM -- D-S evidence theory -- feature fusion -- sEMG; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

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%.