Title: Surface EMG-based human-machine interface that can minimise the influence of muscle fatigue

Authors: Xiaodong Xu; Yi Zhang; Xinli Xu; Huosheng Hu

Addresses: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China ' Engineering Research and Development Centre for Information Accessibility, Chongqing University of Posts and Telecommunications, Chongqing 400065, China ' Engineering Research and Development Centre for Information Accessibility, Chongqing University of Posts and Telecommunications, Chongqing 400065, China ' School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK

Abstract: It is clear that the surface electromyographic-based (sEMG) human-machine interface (HMI) shows a reduction in stability when the muscle fatigue occurs. This paper presents an improved incremental training algorithm that is based on online support vector machine (SVM). The continuous wavelet transform is used to study the changes of sEMG when muscle fatigue occurs, and then the improved online SVM is applied for sEMG classification. The parameters of the SVM model are adjusted for adaptation based on the changes of sEMG signals, and the training data is conditionally selected and forgotten. Experiment results show that the presented method can perform accurate modelling and the training speed is increased. Furthermore, this method effectively overcomes the influence of muscle fatigue during a long-term operation of the sEMG-based HMI.

Keywords: human-machine interface; HMI; surface EMG; sEMG; electromyograms; muscle fatigue; online SVM; support vector machines; incremental training; continuous wavelet transform; CWT; modelling.

DOI: 10.1504/IJMIC.2014.066261

International Journal of Modelling, Identification and Control, 2014 Vol.22 No.4, pp.298 - 306

Published online: 27 Dec 2014 *

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