## 3 Results
### sEMG Signal-Based Movement Classification  
Based on surface EMG signal events, iEMG eigenvalues were calculated for six channels in this paper. The 80% of the dataset events were used to train the classifiers (kNN, SVM and ANN) so as to find the optimal parameters that minimize the error function between the estimation and the real label of each event, as shown in Tables 3 and 4. The 20% of the dataset events were used to test the classifiers and evaluate its performance. In turn, it was verified whether two machine learning algorithms that consider sex muscle differences can improve prediction accuracy. In this paper, the test set prediction confusion matrix (7 \(\times\) 7) for different classifiers was built separately for the method of differentiating datasets and the method of adding sex labels, as shown in Figures 3 and 4. The confusion matrices contain the percentage of all events correctly and incorrectly classified per hand-gesture, prediction accuracy, prediction coverage of per hand-gesture and the total prediction accuracy of the classifier. As shown in Figure 3, among the three classifiers (kNN, SVM and ANN), the sex-differentiated dataset (Female, Male) was significantly better than the non-sex-differentiated dataset (Half_F&M) in terms of prediction results. The ANN classifier had the best prediction results, with 98.3% for Female, 97.0% for Male and 96.5% for Half_F&M in the different datasets. As illustrated in Figure 4, in all three classifiers (kNN, SVM and ANN), the prediction results with the addition of the sex label (Sum_Label) were significantly better than those without the addition of the sex label (Sum). The best predictions were obtained for the ANN classifier, with predictions of 98.4% for Sum_Label and 97.9% for Sum. Figure 4 shows the overall prediction accuracy by aggregating the confusion matrices of the test sets in Figures 3 and 4. For the kNN and SVM classifiers, the prediction accuracy of the differentiated dataset was higher than that of adding a sex label, as shown in Figure 5. For the ANN classifier, the prediction accuracy of adding a sex label was higher than that of differentiating-the-sex dataset. Overall, machine learning algorithms that consider sex muscle differences can improve the prediction accuracy of hand gesture recognition, with the ANN classifier adding a sex label having the highest prediction accuracy of 98.4%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Type III Sum of** & \multirow{2}{*}{**df**} & \multirow{2}{*}{**Mean Square**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**Sig.**} \\  & **Squares** & & & & \\ \hline Corrected Model & a35,886,260,844.381 & 71 & 505,440,293.583 & 589.041 & 0.000 \\ Intercept & 28,887,146,915.346 & 1 & 28,887,146,915.346 & 33,665.138 & 0.000 \\ MT & 7,338,848,044.619 & 5 & 1,467,769,608.924 & 1710.542 & 0.000 \\ SEX & 354,125,990.425 & 1 & 354,125,990.425 & 412.699 & 0.000 \\ MUSCLE & 14,477,477,144.689 & 5 & 2,895,495,428.938 & 3374.416 & 0.000 \\ MT \(*\) SEX & 56,599,280.705 & 5 & 11,319,856.141 & 13.192 & 0.000 \\ MT \(*\) MUSCLE & 12,753,904,623.649 & 25 & 510,156,184.946 & 594.537 & 0.000 \\ SEX \(*\) MUSCLE & 216,823,324.470 & 5 & 43,364,664.894 & 50.537 & 0.000 \\ MT \(*\) SEX \(*\) MUSCLE & 332,584,288.419 & 25 & 13,303,371.537 & 15.504 & 0.000 \\ Error & 14,595,822,050.544 & 17,010 & 858,073.019 & & \\ \hline \hline \end{tabular}  
*R Squared = 0.711 (Adjusted R Squared = 0.710).  
\end{table} 
Table 7: Tests of between-subject effects (dependent variable: iEMG value). Figure 4: Three classifiers (kNN, SVM and ANN) were applied to the confusion matrix (7 \(\times\) 7) generated by adding a sex labeling method for test-set hand gesture recognition. Precision represents the prediction accuracy of each gesture, and Recall represents the predicted coverage of each gesture. Precision \(=\frac{\text{TP}}{\text{TP+FP}}\times 100\%\) Recall \(=\frac{\text{TP}}{\text{TP+FN}}\times 100\%\) where TP = true positive; FP = false positive; and FN = false negative.