Affiliations: Department of Mechanical Engineering, Anhui University of Technology, Maanshan, Anhui, China
Correspondence:
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Corresponding author: Lu Wang, Department of Mechanical Engineering, Anhui University of Technology, Maanshan, Anhui 243002, China. Tel.: +86 0555 2282868; Fax: +86 0555 2316515; E-mail: ahutwanglu@aliyun.com.
Abstract: The recorded surface Electromyogram (sEMG) signals is inevitably influenced by noise during the signal acquisition process. Because of the constant deviation that is known as the Pseudo-Gibbs phenomenon, which exists in conventional wavelet threshold de-noising, noise pertaining to sEMG is inevitable. In order to avoid or minimize noise in the recorded sEMG signals, the objective of this study is to investigate the feasibility of developing a new de-nosing function that uses the advantage of the adaptive threshold adopted in the Brige-Massart algorithm and test the performance of the new function. The simulation results show that applying this new de-noising function could effectively remove noise in the recorded sEMG signals. Furthermore, when compared with conventional threshold de-noising function, the new function achieved higher performed de-noising effect, and thereby enabling better signal analysis subsequently.
Keywords: Surface electromyogram signal, de-noising, threshold function, signal-to-noise ratio
