# Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
## 7 Conclusions  
A two-layer classifier has been proposed to enhance the reliability of the myoelectric control system in this study. Unlike the conventional classifier, the proposed classifier introduces a rejection option to decline the test samples that are not reliable enough. We have compared the proposed classifier with the LDA classifier with a reject option, the GMM classifier with a reject option, and the kNN classifier with a reject option on 12 hand movements. The proposed classifier produces much higher classification accuracy for test samples from trained movements than the other classifiers at the same rejection rate level. The improvement of proposed classifier over the other classifiers in terms of tAcc and aAcc are 9.0%, 2.9%, and 1.8% and 11.2%, 3.7%, and 2.0%, respectively. Though the improvement of the proposed classifier over the kNN classifier is limited compared with the other classifiers, the proposed classifier is much more computationally efficient than the kNN classifier, showing the effectiveness of the proposed model in ambiguity rejection. For test samples from untrained movements, the proposed classifier has a much lower error rate than the other classifiers, proving the effectiveness of the proposed classifier in novelty rejection. The proposed classifier has also been applied to recognize six hand movements. The results show that the tAcc and aAcc of the proposed classifier on trained movements can reach 95.6% and 99.2% when the error rate of untrained movement is 20.9%. The tAcc and aAcc of trained movements can be 95.3% and 99.2% by adjusting the threshold. The error rate of untrained movement is only 17.4%, showing a significant improvement in recognizing trained movements and rejecting the untrained movements compared with previous studies. Not applicable. The data presented in this study are available in the link: [[http://ninapro.hevs.ch/node/3](http://ninapro.hevs.ch/node/3)]([http://ninapro.hevs.ch/node/3](http://ninapro.hevs.ch/node/3)) (accessed on 17 February 2022). The authors declare no conflict of interest. * [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON]; [PERSON] Effect of arm position on the prediction of kinematics from EMG in amputees. _Med. Biol. Eng. Comput._**2013**, _51_, 143-151. [CrossRef]
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