Method for identifying surface electromyography (sEMG) on basis of empirical mode decomposition (EMD) sample entropy
A technology of myoelectric signal and recognition method, applied in the field of pattern recognition, which can solve the problems of real-time control of myoelectric prosthetic hand and weak anti-noise ability
Active Publication Date: 2013-03-13
HANGZHOU DIANZI UNIV
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The invention provides a method for identifying surface electromyography (sEMG) on the basis of empirical mode decomposition (EMD) sample entropy. The method comprises the following steps: acquiring the corresponding sEMG from the related muscle tissue; performing EMD by using an actuating signal of the sEMG determined by energy threshold; adaptively selecting a plurality of intrinsic mode function (IMF) components comprising electromyographic signal effective information according to a frequency availability method; superposing the IMF components to serve as effective electromyographic signals and evaluating the sample entropy; and inputting the sample entropy serving as feature vector into a clustering classifier based on a spindle kernel clustering algorithm to realize identification on an upper limb multi-locomotion mode of the electromyographic signal. The sample entropy can disclose the complexity of the sEMG from a short time sequence, represents the tiny change condition of the electromyographic signal well, has high antijamming capability, simple algorithm and high calculation speed, and is particularly suitable for real-time processing of the electromyographic signal.
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