## 1 Introduction  
In recent decades, the incidence of upper extremity dysfunction caused by stroke has remained high; therefore, rehabilitation training and motor function remodeling of upper extremities has become a key research scope in the field of medical rehabilitation. An upper limb rehabilitation exoskeleton can effectively solve the issues of low efficiency, poor repeatability, and high cost of traditional rehabilitation training [1]. It can obtain accurate training data through various sensors [2], constantly update and formulate scientific and reasonable rehabilitation training methods, and allow for better deployment of follow-up treatment plans for patients to provide patients with more accurate treatment plans and improve the rehabilitation training effect. A rehabilitation exoskeleton helps patients to perform relevant actions, which requires motion recognition and motion intent detection. During the process, relevant signals can be obtained through needle electrodes, surface electrodes [3, 4, 5], electroencephalography (EEG), pressure sensors, attitude sensors, pulse wave sensors, etc. [PERSON] provided a definition for motion intent, aimed to systematically review current methods for sensing intent with a comparison of different methods, and proposed the possibility of combining the methods. This provides essential information on addressing task goal interference or predicting future actions [6]. Since sEMG has the characteristics of being advanced, highly correlated, safe, and easy to obtain, it was studied regarding the motion recognition of wrist, shoulder, and elbow joints of upper limbs, and some achievements were obtained. Then established a standard muscle balance model based on the Hill model [7]. Based on the analysis of the Hill-type muscle model, muscle-related parameters were used to characterize the relationship between EMG signals and dynamics-related quantities, and the nerve-muscle-skeletal excitation was further established. The system obtains joint moments and related physical quantities through contraction dynamics [8]. [PERSON] et al. [9] proposed an actuated glove to detect the wearer's motion intent, which was estimated using a machine learning algorithm based on muscle activity. [PERSON] et al. [10] proposed a knee joint angle prediction model based on sEMG signals, which further combined the Gaussian process model with the nonlinear extrinsic input auto-regressive (NARX) model. A non-parametric probabilistic model was developed based on the physiological properties of muscle activation to achieve an accurate prediction of human motion. [PERSON] et al. [11] proposed an alternative solution for mathematical muscle modeling using regression recurrent neural network (RNN)- and convolutional neural network (CNN)-based sEMG signals and completed an elbow joint angle prediction experiment. [PERSON] et al. [12] used support vector machine classifiers and genetic algorithms to classify gestures, focusing on the impact of different sEMG feature weights on the classification of similar grasping gestures. The results show that similar gestures have different sensitivities to sEMG features with different weights. [PERSON] et al. [13] completed the related research on the energy kernel characteristics of an sEMG signal and proposed to analyze the energy kernel phase diagram of an sEMG signal based on a convolutional neural network to complete the process of gesture recognition. [PERSON] et al. [14] proposed a pattern recognition approach for the shoulder to solve the motion intention detection problem based on sEMG. Linear discriminant analysis (LDA) and multinomial logistic regression (MLR) were used to classify a motion intention model with different feature sets. The experimental results demonstrated that the pattern recognition control is sufficient for solving the motion intention detection problem.