Open AccessResearch on Modified Wavelet Threshold Denoising Algorithm Based around SEMG Signal
Author(s) -
Meng Wang,
Keyong Deng,
Leilei Gao,
Hao Wang,
Zhijun Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1880/1/012004
Subject(s) - noise reduction , threshold limit value , wavelet , signal (programming language) , algorithm , function (biology) , mathematics , reduction (mathematics) , pattern recognition (psychology) , computer science , artificial intelligence , speech recognition , programming language , medicine , geometry , environmental health , evolutionary biology , biology
The surface electromyography (SEMG) signal of the lower limbs is collected by attaching electrodes to the surface of the muscles of the lower limbs, so it is non-invasive and simple to operate. Selected the threshold and the threshold function is very important when used the wavelet threshold denoising method to process some non-linear SEMG signals. But the traditional threshold is a fixed value, which is not conducive to the improvement of the denoising effect. Present paper discusses the previous wave threshold denoising means aiming at their shortcomings, to this end, an improved threshold method is proposed in present paper, which can change the threshold with the number of decomposition layers. In the meantime, the shortcomings of the previous threshold functions can be overcome by the modified threshold function. The SNR and MSE is selected as the parameters to evaluate the denoising performance and comparing the modified wavelet threshold denoising arithmetic with the traditional threshold arithmetic, the experiment results indicated that the denoising impact of the modified arithmetic is better than the previous arithmetic and it has certain practical value.