A novel method to process surface electromyography signal for pedestrian lower limb motion pattern recognition. (September 2020)

• Record Type: Journal Article
• Authors: Gu, Chenyang; Ren, Chunhua; Zhou, Meilin
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 42:Number 13(2020)
• Page Start: 2492
• Page End: 2498
• Publication Date: 2020-09
• Subjects: sEMG -- motion pattern recognition -- signal processing method; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331220918357
• Languages: English
• ISSNs: 0142-3312

Abstract

Surface electromyography (sEMG) signals mainly contain power line interference (PLI), white Gaussian noise (WGN), and baseline wandering (BW) noise. These noises lead to the problems of poor feature extraction performance and low recognition rate. In this paper, we propose a novel sEMG signal processing method named filtering and self-enhancement algorithm with classical wavelet (FSECW) algorithm, which denoises interference noises of raw signals and improve the pedestrian motion pattern recognition rate. The proposed FSECW algorithm contains two core parts: in the first part, the original signal is reconstructed after four-layer wavelet decomposition. This part decreases the BW noise and enhances the active segment; in the other part, band-pass filtering and lifting wavelet transformation are used to reduce noises inside and outside the sEMG signal band. Then two signals from the above parts are multiplied. Thus, the enhanced filtered signal of the active segment is obtained. After feature extraction, the algorithm uses classical machine learning algorithm for motion pattern recognition. Experimental results show that the proposed FSECW algorithm does not need to set different thresholds for different data sets with the same motion pattern. Moreover, it has better adaptability to conversions of different motion patterns.