Real-time modeling and feature extraction method of surface electromyography signal for hand movement classification based on oscillatory theory

Abstract

Objective. Research of surface electromyography (sEMG) signal modeling and feature extraction is important in human motion intention recognition, prosthesis and exoskeleton robots. However, the existing methods mostly use the signal segmentation processing method rather than the point-to-point signal processing method, and lack physiological mechanism support. Approach. In this study, a real-time sEMG signal modeling and separation method is developed based on oscillatory theory. On this basis, an sEMG signal feature extraction method is constructed, and an ensemble learning method is combined to achieve real-time human hand motion intention recognition. Main results. The experimental results show that the average root mean square difference value of the sEMG signal modeling is 0.3838 ± 0.0591, and the average accuracy of human hand motion intention recognition is 96.03 ± 1.74%. On a computer with Intel (R) Core (TM) i5-8250U CPU running Matlab 2016Rb, the execution time for the sEMG signal with an actual duration of 2 s is 0.66 s. Significance. Compared with several existing methods, the proposed method has better modeling accuracy, motion intention recognition accuracy and real-time performance. The method developed in this study may provide a new perspective on sEMG modeling and feature extraction for hand movement classification.