# Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
## 6 Discussion  
The proposed algorithm can enhance the classification accuracy by rejecting the movement with low reliability and unknown movement patterns. A classification scheme derived from the multi-class problem and through the linear programming boosting algorithm (MCLPBoost) was proposed in [37] to enhance the classifier's robustness against untrained classes. We have compared the performance of the proposed algorithm with the one in [37]. Six most-similar hand movements were selected from Exercise B, Ninapro database 1 as t used in [37]. The six movements were wrist flexion, wrist extension, wrist promotion, wrist supination, fingers flexed together in a fist, and abduction of all fingers  
Figure 8: Classification performance on 12 untrained movements. The parameters of the classifiers are the same as in Figure 5.  
Figure 7: The average time cost of recognizing one movement by each classifier. Each histogram is normalized so that the summation of the height of the bins is one. (**a**) Comparison of the time cost of GMM-R and kNN-R. (**b**) Comparison of the time cost of GK-R and kNN-R.  
(corresponds to hand open) [35; 36], where the total number of subjects was also 27. Though a different dataset was applied, it is shown that with the same classifier parameters used above, the average error rate of the six untrained movements obtained is 23.7%, which is comparable to the one obtained in [37]. The proposed GK-R classifier is trained with the first five repetitions of the six movements and tested with the left five for trained hand movements. It is shown that the total accuracy of the trained movements obtained is 95.6%, and the aAcc is 99.2%, which is much higher than the reported 80% accuracy, showing a significant improvement in rejecting unknown movements and high accuracy for trained movement patterns. The GK-R classifier proposed in this study consists of two layers. The first layer classifier GMM gives a preliminary judgment on the test samples. If its threshold is too large, then more test samples will be passed to the second layer classifier kNN for a final decision, which may help to increase classification accuracy but at a higher computational cost. In contrast, if the threshold is small, the classification accuracy is dependent mainly on the GMM classifier. In the extreme case, when the threshold is small enough, the classifier's performance is entirely determined by the GMM classifier, and no test samples will be rejected. So delicately choosing the value of \(\delta_{\text{g}}\) is kind of important. The value of \(\delta_{\text{g}}\) determines which index is more valued, computational cost or classification accuracy. For a fixed \(\delta_{\text{g}}\), the parameter \(\delta_{\text{k}}\) of the second layer classifier determines the rejection ratio to some extent. Figure 9 shows the performance of the proposed classifier on the six trained and untrained movements. The parameter \(\delta_{\text{g}}\) varies from 55% to 65% with an increment of 5%, and \(\delta_{\text{k}}\) increases from 65% to 95% with an increment of 5%. From Figure 9a, it can be found that the aAccs of different values of \(\delta_{\text{g}}\) overlap with each other, and they are all 99.2%. Though the tAccs have different values, they show a similar tendency toward the increase of \(\delta_{\text{k}}\). For a smaller \(\delta_{\text{g}}\), more decisions are made by the first layer classifier, and fewer samples are rejected, which will result in a higher tAcc, as shown in Figure 9a. However, the side effect of a smaller \(\delta_{\text{g}}\) is that the error rate of untrained movements will increase, as shown in Figure 9b. Therefore, when choosing the value of \(\delta_{\text{g}}\) and \(\delta_{\text{k}}\), a compromise should be made between the accuracy of trained movements and the error rate of untrained movements. From Figure 9a,b, it can also be found that when the error rate of the untrained movements is 20.9%, the tAcc and aAcc of the trained movements are 95.6% and 99.2%, respectively.