A sparse Bayesian learning based scheme for multi-movement recognition using sEMG

Abstract

This paper proposed a feature extraction scheme based on sparse representation considering the non-stationary property of surface electromyography (sEMG). Sparse Bayesian learning was introduced to extract the feature with optimal class separability to improve recognition accuracy of multi-movement patterns. The extracted feature, sparse representation coefficients (SRC), represented time-varying characteristics of sEMG effectively because of the compressibility (or weak sparsity) of the signal in some transformed domains. We investigated the effect of the proposed feature by comparing with other fourteen individual features in offline recognition. The results demonstrated the proposed feature revealed important dynamic information in the sEMG signals. The multi-feature sets formed by the SRC and other single feature yielded more superior performance on recognition accuracy, compared with the single features. The best average recognition accuracy of 94.33% was gained by using SVM classifier with the multi-feature set combining the feature SRC, Williston amplitude (WAMP), wavelength (WL) and the coefficients of the fourth order autoregressive model (ARC4) via multiple kernel learning framework. The proposed feature extraction scheme (known as SRC + WAMP + WL + ARC4) is a promising method for multi-movement recognition with high accuracy.

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“…Cheng et al 19 proposed an adaptive group sparse Bayesian learning method for uplink broadband channel estimation, which can effectively utilize part of joint sparsity shared by each channel vector, thus significantly improving channel estimation performance. Ding et al 20 used sparse Bayesian method to extract feature sparse representation coefficients, which can represent the features of surface electromyography signals with non-stationary characteristics well and improve the recognition accuracy of multi-movement patterns. In addition, Heteroscedastic Gaussian Process (HGP) is another algorithm which can better deal with non-stationary data signals.…”