## 1 Introduction  
The movement intention of the human body is generated from the brain and transmitted to the muscle cells through the nerves. The form and amplitude of the electrical signal of the muscle directly reflect the movement pattern of the human body [1; 2; 3]. In order to enable wearable devices such as prosthetics and exoskeletons to switch smoothly between multiple movement modes, many scholars have worked on the recognition of the human movement state. An effective method to identify human movement patterns and movement intentions is by collecting human electromyography (EMG) signals to control the movement of an exoskeleton in real time [4,5]. [PERSON] et al. [6,7] trained a model to recognize amputees in different motion modes through mechanical signals and EMG signals. The lower-limb prosthetic system is expected to seamlessly switch among motion modes, and the overall recognition rate can be increased to 86%. [PERSON] et al. [8] used the Bayesian information criterion (BIC) and some standard feature extraction methods and linear discriminant analysis (LDA) classification algorithms to separate eight different gait phases based on the electromyogram (EMG) signal data of the lower limbs. [PERSON] et al. [9] used pattern recognition to seamlessly and naturally switch among the five motion modes of prosthetics. Collecting and analyzing EMG signals can help the exoskeleton to accurately identify the current state of movement of the human body for matching the best movement mode [10, 11, 12, 13]. [PERSON] et al. [14] used the data obtained by the angle sensor to train the model to achieve better recognition results. [PERSON] et al. [15] used myoelectric sensors, gyroscopes, and pressure sensors to collect data and used a hidden Markov model (HMM) to identify real-time motion states. In this way, the distinction in the intention of the user to walk on five different terrains can be inferred. Capturing images of human motion to analyze motion patterns is a very mature method, but this method requires huge image acquisition equipment and cannot be integrated with wearable devices such as an exoskeleton [16].  
We have tried to install a gyroscope on each joint of the lower limbs of the human body, install a pressure sensor on the selected position of the foot, and analyze the human movement pattern by multi-source information fusion, which has showed a good recognition effect on the human movement pattern recognition [17]. Since the angle information of the lower limbs of the human body and the pressure information of the sole of the foot are generated after the actual movement of the human body, there is a lag in the judgment of the human body's movement intention and is rather more suitable for identifying the current movement mode of the human body or exoskeleton system. The surface EMG signal of the muscle has the advantage of not being restricted by physical constraints and, thus, not disturbing the actual movement of the human body. Therefore, in order to improve the accuracy of the recognition of common human movement patterns in the daily use of exoskeletons, we designed a wearable EMG acquisition system to collect EMG signals on the surface of the human body and identify common human lower-limb movement patterns. A supervised machine-learning method is used to train a motion pattern classifier, by using the surface EMG signals, to study the effects of different feature parameters on human motion pattern recognition based on multilayer perceptrons and the long short-term memory (LSTM) neural network.