## 4 Discussion  
We know that when the human body wears an exoskeleton, the accurate recognition of the human movement mode guarantees the switching to the accurate movement mode during the mixed movement control of the exoskeleton. Scholars have tried various methods to identify the movement state of the human body and obtain the accurate movement intention of the human body. [PERSON] et al. [6; 7] trained an algorithm to recognize amputees in different motion modes through mechanical and EMG signals. The overall recognition rate using EMGs can be increased to 86%. [PERSON] et al. [8] used the Bayesian information criterion (BIC) along with some standard feature extraction methods and linear discriminant analysis (LDA) classification algorithms to separate eight different gait phases by using the electromyogram (EMG) signal data of the lower limbs, and the maximum accuracy of recognition of the left and the right leg motion patterns was found to be 93.83% and 91.60%, respectively. [PERSON] et al. [9] used pattern recognition to seamlessly and naturally switch control among multiple motion modes of prosthetics. Under five movement modes, the recognition error rate was less than 5%. [PERSON] et al. [14] used angle sensors to obtain human motion data, and the algorithm was trained to achieve the model on the test set with an average accuracy of 87.22%. [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. The intention of the prosthetic user to walk on different terrains can be inferred, and the intent pattern recognizer can recognize five typical terrain patterns with an accuracy of 95.8%. We have tried to install gyroscopes on various joints of the lower limbs of the human body and installed pressure sensors at key positions on the sole of the feet. The accuracy of identifying mixed human movement patterns was 92.7% [17]. In this paper, different feature parameters of human surface electromyographic signals are extracted in the seven daily motion modes. Two common neural networks, multilayer perceptrons and LSTM neural networks, are separately trained to identify human motion patterns. In the training process, different feature parameters and different classifier model parameters were used to compare the accuracy of the motion pattern recognition and error impact analysis. In our work done in this paper, the best recognition rate trained by the multilayer perceptrons was 95.53%, and the best recognition rate trained by the LSTM neural network was 96.57%. Compared with the motion recognition rate of the above scholars, it has a slight advantage. At the same time, the motion data acquisition system is compact and easy to integrate with the exoskeleton. In this study, the reason for the lower accuracy of the training results might be the dataset being not very large. For example, the local minimum in Figure 5a appears to have some \"cursed\" numbers of hidden neurons. So, in our next work, we intend to add more datasets for the training to nullify this problem. During the establishment of the feature parameters dataset, we found that the data's absolute mean value (MAV) and the root mean square (RMS) have remarkable similarity, so one of them can be deleted for dimensionality reduction. Generally, we think that the calculation cost of RMS will be high, but we have deleted the mean absolute value (MAV) instead of the root mean square (RMS). The reason is that the amount of data processed was not very large. Of course, it is precisely because of the amount of data this time being not very large that leads to the final motion pattern recognition rate being lower. In the process of mass data processing in the future, we will improve this and reconsider the feature value selection in the process of establishing a feature parameters dataset. There are five activation functions in a typical LSTM neural network, including three sigmoid and two tanh.