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Joint Motion Prediction Based on sEMG Third-order Cumulant Feature

In order to solve the problem that the surface electromyography signal is not accurate enough for the quantitative identification of the human motion angle, this paper used PCA-based third-order cumulant analysis method to extract sEMG signal features, and used this features as input for a single-layer BP neutal network to predict the joint angle of the human body. In the identification of shoulder joint motion, the root mean square error of the prediction result was 5.19, and the correlation coefficient was 0.95, which is obviously superior to the AR model method and the RMS method. Introduction Motion recognition technology is one of the key technologies of the exoskeleton system, which related to the control effect of the exoskeleton device. Since the generation of the human electromyography signal precedes the skeletal muscle contraction for about 100ms, it can quantitatively reflect the muscle contraction state, and quantitatively estimate the continuous motion [1]. Therefore, the sEMG signal can be used to predict the motion of the human body, which is crucial for the motion recognition of the exoskeleton system. The research on motion state recognition based on sEMG mainly includes two aspects: signal feature extraction method and multi-mode classification algorithm. Feature extraction methods include time domain analysis, frequency domain analysis, time-frequency analysis, and high-order cumulant analysis. The extraction process of time domain statistical features is the simplest. The main methods include mean value, root mean square (RMS), integral electromyography (IEMG) and Auto Regressive (AR) model. The frequency domain analysis include Median Frequency (MDF) and Mean Frequency (MNF). Neural networks are the most common in pattern classification methods, and BP neural network is the most widely used. Sun Xin and Li Jinming of Harbin Institute of Technology quantitatively identify the angles of the elbow joint and the knee joint by RMS and MDF eigenvalues, respectively [2], [3]. Li Chenglong of Wuhan University of Technology uses the AR model to identify the action patterns of the fingers and lower limbs [4]. In the study of motion state identification by sEMG, there are many researches on the classification of motion patterns, and the error of identification results is small. However, there are few studies on quantitative identification of motion angles, and the error of identification results is large. This is because the traditional signal feature detection method has two main disadvantages: (1) time domain features, frequency domain features and time-frequency features are applied to the analysis of stationary signals, and there are limitations on the detection of non-stationary signals, (2) the first-order and second-order statistical analysis can only depict the Gaussian process, the detection effect will be significantly reduced when the noise of the detected signal is large. Therefore, selecting the appropriate signal features as the input for the BP neural network can avoid the shortcomings of the time-frequency domain signal characteristics and improve the prediction accuracy. At the same time, due to the advancement of the sEMG signal, the sEMG signal can be used as the human-machine interface. The exoskeleton system can get an accurate and timely response.