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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

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De-noising surface electromyograms using an adaptive wavelet approach

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