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1

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

2

Engineering Research & Development Center for Information Accessibility, Chongqing University of Posts and Telecommunications, Chongqing 400065, China





*

Author to whom correspondence should be addressed.



Abstract In this paper, an adaptive human-machine interaction HMI method that is based on surface electromyography sEMG signals is proposed for the hands-free control of an intelligent wheelchair. sEMG signals generated by the facial movements are obtained by a convenient dry electrodes sensing device. After the signals features are extracted from the autoregressive model, control data samples are updated and trained by an incremental online learning algorithm in real-time. Experimental results show that the proposed method can significantly improve the classification accuracy and training speed. Moreover, this method can effectively reduce the influence of muscle fatigue during a long time operation of sEMG-based HMI. View Full-Text

Keywords: intelligent wheelchair; sEMG; incremental support vector machine; human-machine interaction intelligent wheelchair; sEMG; incremental support vector machine; human-machine interaction





Autor: Xiaodong Xu 1,* , Yi Zhang 2, Yuan Luo 2 and Dongyi Chen 1

Fuente: http://mdpi.com/



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