# Surface Electromyography-Based Action Recognition and Manipulator Control
## 5 Discussion  
From Figure 6 and Table 1, the effectiveness of the single feature to the action classification could be judged preliminary. The time-domain features and frequency-domains feature had more intuitive \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Action** & **First Clenching** & **Hand Opening** & **Wrist Flexion** & **Wrist Extension** & **Calling Me** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Fist clenching \\ Hand opening \\ \end{tabular} } & 950 & 0 & 0 & 0 & 0 \\  & 34 & 606 & 49 & 171 & 90 \\  & 8 & 22 & 877 & 27 & 16 \\  & 10 & 16 & 34 & 883 & 7 \\  & 38 & 76 & 120 & 89 & 627 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix evaluation of GA–SVM. The classification results are further refined in this table. Similarly, the diagonal elements represent the number of correctly classified samples, which meets the accuracy in Table 2 as well. Other elements mean the number of samples that are misclassified. It is obvious that the classification accuracy of the GA–SVM is lower than that of the BP neural network, and the misjudged samples are greater in number. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Average** & **First Clenching** & **Hand Opening** & **Wrist Flexion** & **Wrist Extension** & **Calling Me** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} MAV \\ ZC \\ WL \\ \end{tabular} } & 90.0\% & 100\% & 97\% & 79\% & 93\% & 81\% \\  & 27.4\% & 21\% & 19\% & 17\% & 47\% & 33\% \\  & 87.0\% & 98\% & 81\% & 77\% & 93\% & 86\% \\  & 87.2\% & 95\% & 93\% & 76\% & 91\% & 83\% \\  & 38.8\% & 22\% & 21\% & 34\% & 71\% & 46\% \\  & 37.2\% & 18\% & 47\% & 19\% & 59\% & 43\% \\  & 71.6\% & 87\% & 71\% & 62\% & 75\% & 63\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification accuracy of 5 actions (first clenching, hand opening, wrist flexion, wrist extension, and calling me) based on single feature (MAV, ZC, WL, RMS, MF, MPF, and wavelet coefficient). The purpose of this table is to explore the optimal feature among all features selected. Each row represents the accuracy of different actions based on a single feature. Each column represents the accuracy of a single action based on different features. We also calculated the average accuracy to make a better comprehensive comparison. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Average** & **First Clenching** & **Hand Opening** & **Wrist Flexion** & **Wrist Extension** & **Calling Me** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} BP \\ GA-SVM \\ \end{tabular} } & 93.2\% & 100\% & 92\% & 91\% & 96\% & 87\% \\  & 83.0\% & 100\% & 64\% & 92\% & 93\% & 66\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Three-feature-combination joint action classification of BP neural network and GA–SVM. This table explores the performance of the BP neural network and the GA-SVM, based on the same features (MAV, WL, and RMS). Each row represents the accuracy of different actions based on a certain classifier. Each column represents the accuracy of a single action, based on different classifiers. We also calculated the average accuracy for better comprehensive comparison. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Action** & **First Clenching** & **Hand Opening** & **Wrist Flexion** & **Wrist Extension** & **Calling Me** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Fist clenching \\ Hand opening \\ \end{tabular} } & 950 & 0 & 0 & 0 \\  & 9 & 876 & 17 & 27 & 21 \\  & 16 & 22 & 865 & 29 & 18 \\  & 5 & 12 & 15 & 911 & 7 \\  & 20 & 25 & 43 & 36 & 826 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix evaluation of the BP neural network. The classification results are further refined in this table. The diagonal elements represent the number of samples classified correctly, which meets the accuracy in Table 2. Other elements mean the number of samples that are misclassified. More importantly, through the confusion matrix, it is clear to see which pattern has been misclassified for a certain sample.