Classification of hand movements using variational mode decomposition and composite permutation entropy index with surface electromyogram signals

Abstract Research of human hand movements recognition can be applied to artificial limb control, motion recognition of wearable exoskeleton, human–computer interaction in virtual reality and so on. Surface Electromyogram (sEMG) signal is the preferred source. There are many researches on how to extract information from sEMG signal and apply it to human motion recognition. However, how to extract the feature signal from sEMG signal is a difficult problem in the research of human hand movement recognition based on sEMG signal. In this paper, a method based on Variational Mode Decomposition (VMD) and composite permutation entropy index (CPEI) method is proposed for hand motion classification. Previously, the VMD method had not been used in human hand motion recognition studies. The method proposed in this work applies the VMD method to decompose the original sEMG signal into multiple Variational Mode Functions (VMFs) and calculate the corresponding CPEI of each signal component. Three feature selection methods (Infinite Latent Feature Selection (ILFS), ReliefF, and Laplacian Score) were applied to rank the features and remove the unimportant features. Three classifiers (Naive Bayes, K-NN, and Bagging) were used to recognize the hand actions. Ten volunteers participated in the experiment, and the experimental data were used to verify the proposed method. The average accuracy was 94.28 ± 1.26% for the proposed method with Laplacian Score for feature sorting and selection, and Bagging as classifier. Besides, 600 randomly selected hand movements are predicted (CPU is i5-8250U, ram is 8g, processing software is Spyder, python 3.7), and the corresponding execution time of proposed method is 0.56 s.

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