Single-channel surface electromyography signal classification with variational mode decomposition and entropy feature for lower limb movements recognition

• Published in: Biomedical signal processing and control Vol. 74; p. 103487
• Main Authors: Wei, Chunfeng, Wang, Hong, Hu, Fo, Zhou, Bin, Feng, Naishi, Lu, Yanzheng, Tang, Hao, Jia, Xiaocong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2022
• Subjects: Lower limb movements recognition; Machine learning; Surface electromyography; Variational mode decomposition; Variational mode decomposition; Surface electromyography; Lower limb movements recognition; Machine learning
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2022.103487

•We propose a precise feature extraction method based on VMD to highlight the prominent structure and effective feature information in the single-channel sEMG signal.•The recognition performance of the proposed method on the sEMG signal of two muscles is evaluated.•The accurate recognition of lower limbs movements based on single-channel sEMG signals improves the usability of lower limbs wearable devices based on sEMG signals in amputees, patients with impaired muscle function, and the disabled.•Using only sEMG signal sensors effectively avoids system complexity problems caused by multi-sensor fusion. Currently, many researchers tend to use multi-channel surface electromyography (sEMG) signals to improve the accuracy of lower limb movement recognition. However, the collection of multi-channel sEMG signals will reduce the usability of wearable devices for lower limbs based on sEMG signals in amputees, patients with impaired muscle function, and the disabled. How to effectively use single-channel sEMG signals to achieve better recognition performance is a difficult problem to improve the usability of wearable devices based on sEMG signals. In this research, we proposed a precise feature extraction method for single-channel sEMG signals to achieve accurate recognition of lower limb movements. The single-channel sEMG signal was decomposed into multiple variational modal functions (VMF) through variational mode decomposition (VMD), and entropy features were extracted from VMFs to highlight the prominent information of the sEMG signal. Entropy features with statistical differences were selected by the Kruskal-Wallis test. Four lower limb movements were recognized through machine learning. Moreover, the recognition performance exhibited by the proposed method on the sEMG signal of two different muscles was evaluated. The sEMG signals of four lower limb movements from twenty subjects recorded by the wearable sEMG signal sensor were employed to test the proposed method. The experimental results showed that the accuracy of the proposed method for the sEMG signals of two different muscles reached 95.82% and 97.44%. This research concluded that the proposed method is promising to improve the usability of wearable devices based on sEMG signals in amputees, patients with impaired muscle function, and the disabled.