# Surface Electromyography-Based Action Recognition and Manipulator Control
## 5 Discussion
spatial distribution characteristics so that the contribution to action recognition could be evaluated with the naked eye. In the position distribution of MAV, WL, and RMS, we could see that the points of 5 actions was scattered comparatively. The points representing any 2 actions were independent. The space was large and there was very little overlap. Nevertheless, in the distribution of ZC, MF, and MPF, almost all points representing all 5 actions were mixed together, and it was almost impossible to distinguish each other. Table 1 further confirmed the effectiveness of all features. The classification rate of MAV was the best among all features--the average accuracy rate was 90.0%. Comparing the 5 actions to be recognized, the accuracy of fist clenching could reach to 100%, which really realized the ideal classification. The WL and RMS could also achieve high accuracy in general. By contrast, the ZC, MF, and MPF contributed less to action classification. Although the wavelet coefficient contributed an accuracy of 71.6%, its classification accuracy was still not as high as that of MAV, WL, and RMS. Considering the effectiveness of the features and the calculation amount, we finally chose MAV, WL, and RMS to do the joint action classification afterwards. From Table 2, the accuracy of different classifiers based on the same features was obvious. By using the BP neural network, it was clear that the recognition accuracy of fist clenching could reach 100%, and that of hand opening, wrist flexion, and wrist extension exceeded 90%. The average accuracy achieved up to 93.2%. With regards to the GA-SVM, the accuracy of fist clenching could still reach 100%, and that of wrist flexion and wrist extension achieved 90% or more, as well. However, the accuracy of hand opening and calling me as well as the overall average accuracy was lower. The confusion matrices further quantified the classification performance. From Tables 3 and 4, not only can we know the classification rate of each action, but we can also we observe the misjudgments in the confusion matrices. Some actions have a higher accuracy while others have a relatively lower accuracy. For instance, the accuracy of calling me was always lower than the other 4 typical actions. When doing the action of calling me, the amplitude and intensity of the muscle movement was lower than other common movements, so it was possible that the acquired signals was difficult to analyze and the features were not so apparent. Additionally, calling me tended to be judged to wrist flexion. It was possible that when doing the action of calling me, the activated muscle area overlapped with the wrist flexion, and the sEMG signals also had similarities, leading to misjudgment. Compared to GA-SVM, the overall classification rate of the BP neural network exceeded 10.2% and the number of misjudgments was relatively smaller. When it came to the exact action, the classification accuracy of the BP neural network exceeded that of the GA-SVM generally. In contrast, we concluded that the BP neural network performed better in action recognition. Our research comprehensively considered a series of factors, first of which was to ensure action recognition accuracy, while improving the response time. First of all, compared to some research [48; 49; 50; 51; 52] that aim at improving accuracy only by using a large number of features or algorithms with high complexity, we utilized as few features as possible to ensure the action recognition rate. This reduced the amount of calculation and did not exert a high computational complexity. In addition to accuracy, the response time of the entire system needed be considered in real-time control. Based on foundation in biological research, humans feel noticeable delay when it exceeds 300 ms [53]. On one hand, owing to the fewer calculations, signal processing could be finished in a shorter time. On the other hand, we adopted overlapping sliding windows to do the real-time signal processing, which greatly decreased the waiting time of the system.