Research on improved FAWT signal denoising method in evaluation of firefighter training efficacy based on sEMG. (February 2022)

• Record Type: Journal Article
• Authors: Li, Yang; Bai, Ke; Wang, Hao; Chen, Simeng; Liu, Xuejun; Xu, Haopeng
• Is Part Of: Biomedical signal processing and control. Volume 72(2022)Part B
• Publication Date: 2022-02
• Subjects: Flexible analysis -- Genetic algorithm -- sEMG denoising -- Wavelet transform; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103336
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Apply FAWT algorithm to signal denoising, and the experiment have shown that this method can effectively remove the noise signal in sEMG. Multiple indicators for assessing signal linear calculation must be processed to obtain composite index, which can effectively represent the signal denoising effect and prevent the loss of effective signals. Proposed an improved and flexible analysis wavelet transform algorithm, using genetic algorithm to optimize the sampling factor in FAWT, and cross-validated the effectiveness of the GA-FAWT algorithm. Abstract: Surface electromyography (sEMG) signals are often mixed with a large amount of noise during the process of acquisition, and to accurately extract the signal characteristics, it is necessary to effectively remove mixed noise. In this paper, an improved flexible analysis wavelet transform (FAWT) algorithm is proposed. The genetic algorithm (GA) is used to optimise the sampling factors in FAWT, and the comprehensive evaluation index CEI was combined to realise the effective denoising of sEMG. In this study, the standard database and measured sEMG of actual firemen are taken as an example to condueriment proves that the improved FAWT algorithm has a good denoising effect on sEMG.