## 1 Introduction  
With the global increase in the older population and the demand for assisted rehabilitation for hemiplegia patients, various exoskeleton rehabilitation assistance robots are being developed. To improve the control performance of the assistance robots, the biggest challenge is to rapidly and accurately recognize the intention of human action [1]. At present, there are mainly the following ways to realize motion intention recognition [2; 3]: mechanomyography (MMG), Surface electromyography (sEMG), electroencephalography (EEG), and electrocardiography (ECG). The surface electromyography (sEMG) signal is a biological electrical signal produced by human muscle contraction. It has been extensively used to obtain human motion intention in wearable robot systems because of its effectiveness, portability, non-trauma, and non-delay features [4; 5; 6; 7]. Different motion modes produce different frequencies and amplitudes of sEMG signals. Therefore, a wealth of information can be drawn from sEMG signals. Researchers have shown that the estimation of joint interaction force based on sEMG is one of the most effective ways to realize motion intention recognition [8]. There are two widely used methods to define the relationship between the sEMG signal and force: parametric-based and nonparametric-based. [PERSON] et al. [9] used the Hill-type musculoskeletal model to design a quantitative method for the representation of the elbow joint force estimation. [PERSON] et al. [10] proposed an interaction force estimate method by using the tendon length and contraction velocity obtained from sEMG and inverse dynamics analysis. [PERSON] et al. [11] presented a forward dynamic neuromusculoskeletalmodel which can be used to estimate and predict joint moments and muscle forces. [PERSON] et al. [12] presented a model which allows the estimation of muscle force from EMG signals associated with a physiology-based model of underlying microtubule dynamics. [PERSON] et al. [13] proposed a force estimation method by combining a biomechanical muscle model with sEMG signal peaks. [PERSON] et al. [14] proposed a muscle-twitch model to estimate the muscle force in fatigue conditions. Nevertheless, since all these methods need to know the exact parameters of the muscle model, the convergence of the parameters is sensitive to computational time and complexity. A nonparametric algorithm is proposed to find the relationship between the sEMG signal and force. As for nonparametric-based methods, machine learning-powered force estimation methods have become an effective tool in muscle force estimation and action recognition. A large variety of deep learning methods have been used in the biomedical field [15; 16; 17; 18; 19; 20; 21; 22; 23]. For instance, [PERSON] et al. [15] systematically studied the feasibility of MMG for estimating the elbow interaction force at the wrist under an isometric contraction by using an artificial neural network in comparison with sEMG. In [16], an SVR model is proposed to estimate knee joint muscle force based on the MMG signal. [PERSON] et al. [17] combined PCA and a Laplacian arrangement to fit the relationship between sEMG and muscle force. [PERSON] et al. [18] proposed a model based on deep learning to estimate kinematic information from multi-channel sEMG signals. [PERSON] et al. [19] presented a novel method based on multi-sensors to realize six gesture recognition tasks. DWT was used to realize the time-domain feature extraction of signals, and WNN was used to realize the action classification task. Experimental results show that the action classification accuracy of the proposed method is 94.67%, and is superior to the traditional ANN network. [PERSON] et al. [20] proposed a CNN-SVM combined model to make use of their advantages for pattern recognition of knee motion. [PERSON] et al. [21] applied a Long Short-Term-Memory (LSTM) neural network model for estimating the acceleration of the knee joint. [PERSON] et al.