## 5 Conclusions  
In response to the need for the exoskeleton to quickly recognize the wearer's movement state during the mixed movement control, this paper studies the impact of different feature parameters extracted from the surface EMG signals on the accuracy of multilayer perceptrons and the LSTM neural network recognition of human motion patterns. Based on the EMG signals extracted from seven common human motion patterns in daily life, the time-domain and frequency-domain features of the collected information are extracted to build a feature parameter dataset for training the classifier. Using multilayer perceptrons and the LSTM neural network to recognize the motion pattern of human lower limbs, the dataset uses different feature parameters and different classifier model parameters to compare and analyze the accuracy of motion pattern recognition and error impact.  
When the offline supervised learning of the surface EMG dataset based on the multilayer perceptrons is carried out with the sliding window length of 1024 ms, and using all the time and frequency-domain feature parameters, the accuracy rate of the human motion pattern recognition is high, reaching 95.53%. The trend term of the surface EMG has a great impact on the accuracy of the recognition and should be removed before the data is used. The time-domain features contribute more to the accuracy of the model recognition than the frequency-domain features. However, in order to achieve the best recognition accuracy, the time-domain and frequency-domain features should be combined. When the sliding window length is shortened, the recognition accuracy rate decreases rapidly, such as 65.98% at 512 ms, which does not satisfy the needs of human motion pattern recognition.  
When an offline supervised learning of the surface EMG dataset based on the LSTM classifier is carried out with the sliding window length of 1024 ms, and using all the time and frequency-domain feature parameters, the accuracy rate of the human motion pattern recognition is slightly higher than that of the multilayer perceptrons, reaching 96.57%. The ReLU function is not suitable as the activation function of the model input layer, and the model recognition accuracy using the sigmoid function and tanh function as the activation functions is higher. When the length of the sliding window is shortened, the accuracy rate is still high, e.g., the recognition accuracy rate is 88.84% at 512 ms, so the LSTM classifier performs better than the multilayer perceptrons in the recognition of human motion patterns.  
Conceptualization, J.S. and A.Z.; methodology, Y.T.; software, J.S. and H.H.; validation, J.S., Z.S., and Y.T.; writing--original draft preparation, J.S.; writing--review and editing, A.Z.; visualization, M.A.A. and Y.T.; supervision, A.Z.; snd project administration, A.Z., X.Z., and G.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program for Intelligent Robots of the Ministry of Science and Technology, grant number 2017 YFB1300505, and the Shenzhen Joint Key Fund Project of the National Natural Fund, grant number U1813212.  
**Conflicts of Interest:** The authors declare no conflicts of interest.