# Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
## 5 Results
### Performance of Proposed Classifier on Trained Movements  
The accuracy-rejection curve proposed in [40] can be used to evaluate the performance of classifiers with rejection options. On the accuracy-rejection plot, accuracy is plotted against the rejection rate. The curve starts from a point (0, a%), where a% is the accuracy of the classifier when there is no rejection. A system with accuracy and rejection rates in the top left quadrant is the most desirable.  
Figure 3: Comparison of classifier performance in recognizing 12 trained movements with different features. (**a**) Average total classification accuracy (tAcc) versus rejection rate. The features applied by different classifiers are root mean square (RMS) and waveform length (WL). (**b**) Average active classification accuracy (aAcc) versus rejection rate.  
Figure 4 shows the classification results on the 12 hand movements with the classifiers mentioned above. The \(\delta_{\text{g}}\) parameter of GK-R classifier varies from 35 to 75 with a step of 5, and \(\delta_{\text{k}}=\delta_{\text{g}}-10\text{:}10\text{:}\delta_{\text{g}}+10\). From Figure 4a,b, it can be seen that the tAcc and aAcc of GK-R are the highest, followed by kNN-R when the rejection rate is equal. Additionally, the accuracy of GMM-R is the lowest of all.  
Figure 4b shows that although the aAcc of the GK-R classifier is lower than GMM-R with the same parameter \(\delta_{\text{g}}\), its rejection rate has been significantly reduced. It can be inferred from Figure 4a,b that the tAcc and aAcc of the GK-R classifier are much higher than GMM-R and kNN-R when their rejection rates are equal. Therefore, the second layer effectively enhances the accuracy in recognizing samples rejected by the first layer. For the same tAcc and aAcc, the rejection rate of GK-R is much lower than that of GMM-R, showing the advantage of GK-R in reducing the rejection rate and increasing recognition accuracy.  
A grid search method has been applied to compare the performance of the three classifiers in Figure 4 at the same rejection rate level. When the rejection rate is 13.0% \(\pm\) 0.5%, the tAcc and aAcc obtained by the classifiers are shown in Figure 5. It can be seen that the average tAcc and aAcc obtained from GK-R (\(\delta_{\text{g}}=65\%\), \(\delta_{\text{k}}=75\%\)) are much higher than those results obtained by the LDA-R, GMM-R, and kNN-R classifiers. The improvements of GK-R in tAcc over LDA-R, GMM-R, and kNN-R are 9.0%, 2.9%, and 1.8%, respectively, and the improvements in aAcc are 11.2%, 3.7%, and 2.0%, respectively.  
Figure 4: Comparison of movement recognition with GMM-R, kNN-R and GK-R with varying parameters. (**a**) Average tAcc versus rejection rate. (**b**) Average aAcc versus rejection rate.  
Figure 5: Comparison of classifier performance on 12 hand movements when the rejection rate is 13.0% \(\pm\) 0.5%. The features used in LDA-R, GMM-R, kNN-R are WL, WL, and RMS, respectively.  
Figure 6 shows movement prediction using GMM, GK-R, and the first layer classifier (GMM-R) of GK-R for one subject performing 12 hand movements with five repetitions. It can be observed from Figure 6a that most of the test samples can be correctly recognized by the GMM classifier. Those samples that are wrongly predicted by the GMM classifier are all rejected by the first layer (GMM-R) classifier, although some samples that are correctly predicted by the GMM classifier but rejected by GMM-R. Figure 6b shows the prediction results with the proposed GK-R classifier. It can be found that the proposed model is effective in reducing the number of misclassifications in GMM with the help of the second layer classifier at the cost of increasing the number of rejections. However, misclassification is a much more severe problem than rejections for practical applications, especially when the rejection rate is limited.