D-S evidential theory on sEMG signal recognition Online publication date: Fri, 21-Apr-2017
by Weiliang Ding; Gongfa Li; Ying Sun; Guozhang Jiang; Jianyi Kong; Honghai Liu
International Journal of Computing Science and Mathematics (IJCSM), Vol. 8, No. 2, 2017
Abstract: In order to promote the accuracy and complexity in the recognition of sEMG signals by classifiers, this paper tells a method based on fused D-S evidential theory. Three features are discussed in the choice of parameters, which includes AR model coefficient, cepstral coefficients and time-domain integral absolute value. D-S evidential theory gets information based on information fusion of multi feature sets and multi classifiers. In recognition phase, many groups of data are used for the training and the rest is for the test. Through the compare of the accuracy in different parameters, the result is shown according to the experiment about the data fusion in D-S evidential theory. Six actions are set to be the samples. According to three characters, the recognition accuracy is compared. The result shows that the fused data method of D-S evidential theory has better accuracy and robustness. The further study is to determine the optimal fusion feature set to make more accurate and higher robustness of the classification.
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