Gait phases recognition based on lower limb sEMG signals using LDA-PSO-LSTM algorithm. (February 2023)

• Record Type: Journal Article
• Authors: Cai, Shibo; Chen, Dipei; Fan, Bingfei; Du, Mingyu; Bao, Guanjun; Li, Gang
• Is Part Of: Biomedical signal processing and control. Volume 80(2023)Part 1
• Publication Date: 2023-02
• Subjects: Gait phase recognition -- Surface electromyography (sEMG) -- Long short-term memory (LSTM) -- Particle swarm optimization (PSO) -- Linear discriminant analysis (LDA); 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.2022.104272
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Surface electromyography is predictive. This paper proposes an sEMG-based gait recognition method LDA-PSO-LSTM. Moderate combinations of time domain, frequency domain, and time–frequency domain features are beneficial for identifying the seven gait phases. In the case of medium speed, the accuracy of gait recognition using the proposed method is the highest. Different dimensionality reduction methods and classifiers are used for comparison to verify the proposed algorithm effect. Abstract: Gait phases are widely used in exoskeleton movement control. Surface electromyography (sEMG) is predictive and plays an important role in gait phase recognition. The purpose of this study is to improve the stability and accuracy of gait recognition methods based on the sEMG signals of lower limbs. First, we presented a LDA-PSO-LSTM algorithm based on feature combination selection and verified its recognition accuracy through experiments. LDA-PSO-LSTM had an average recognition rate of 94.89% and a maximum accuracy of 97.02%. Second, we tested and compared the recognition accuracy of LDA-LSTM (92.17%). Experiments showed that the PSO optimization model had good recognition performance. Finally, we compared LDA-LSTM with all classifier combinations and concluded that the LDA-LSTM method has the highest recognition rate among a series of method combinations. The results indicated that LDA-PSO-LSTM as a classification model has apparent advantages in gait recognition. LDA-PSO-LSTM provides more accurate gait phase results for lower limb exoskeleton control. This method is beneficial to the development of the exoskeleton gait recognition system.