## 3 Results
### sEMG Signal-Based Movement Classification
Figure 3: Three classifiers (kNN, SVM and ANN) were applied to the confusion matrix (7 \(\times\) 7) generated by differentiating-the-sex dataset 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. In addition, as indicated in Figure 5, by plotting the resultant histograms of prediction accuracy for two types of machine learning algorithms, the prediction accuracy of the classifier is significantly higher after differentiating between the sexes. For the kNN classifier, the differentiating-the-sex dataset method had a higher prediction accuracy of 95.5% for females and 95.6% for males, while the adding a sex labeling method had a prediction accuracy of 94%. For the SVM classifier, the prediction accuracy was similar for both methods, with the differentiating-the-sex dataset method having a slightly higher prediction accuracy of 96.3% for females and 93.9% for males, and the adding a sex labeling method having a prediction accuracy of 94.2%. For the ANN classifier, the adding a sex labeling method had a higher prediction accuracy of 98.4%, while the differentiating-the-sex dataset method had a prediction accuracy of 95.5% for females and 95.6% for males. Furthermore, the results of the 5-fold CV and t-test were shown in Figure 6. For the method of sex dataset differentiation, the prediction accuracy for the Female dataset was significantly higher than that for the Half_F&M dataset for all of the kNN, SVM and ANN classifiers; the prediction accuracy for the Male dataset was significantly higher than that for the Half_F&M dataset for both the SVM and ANN classifiers. For the method of adding the sex label, the prediction accuracy of the kNN, SVM and ANN classifiers with the addition of the sex label was significantly higher than that of the non-addition. The prediction accuracy of the kNN classifier was significantly higher for the Female dataset than for the Half_F&M dataset in the sex differentiation dataset, while the mean prediction accuracy of the Male dataset was slightly higher than that of the Half_F&M dataset. Thus, the statistical results show that for the kNN classifier the sex differentiation dataset can significantly improve the prediction accuracy for females, while the prediction accuracy improvement for males is smaller. Further, it can be concluded that the method of sex differentiation dataset can improve the prediction accuracy on kNN classification. Overall, machine learning algorithms that considered sex muscle differences were able to significantly improve the prediction accuracy of gesture recognition, with ANN classifiers having a higher prediction accuracy than kNN and SVM classifiers. Figure 5: Total prediction accuracy statistics of three classifiers (kNN, SVM and ANN) applied to a test set for hand gesture recognition for two methods considering sex differences: (**A**) differentiating-the-sex dataset and (**B**) adding a sex labeling.