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

Content Provider	Springer Nature Link
Author	Ding, Shuai Wang, Liang
Copyright Year	2015
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.
Starting Page	59
Ending Page	69
Page Count	11
File Format	PDF
ISSN	01589938
Journal	Australasian Physics & Engineering Sciences in Medicine
Volume Number	39
Issue Number	1
e-ISSN	18795447
Language	English
Publisher	Springer Netherlands
Publisher Date	2015-11-17
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Surface electromyography (sEMG) Feature extraction Non-stationarity Sparse representation Temporal MMV sparse Bayesian learning (T-MSBL) Biomedicine general Biophysics and Biological Physics Medical and Radiation Physics Biomedical Engineering
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Physics and Astronomy Biophysics Biomedical Engineering

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