# Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
###### Abstract  
Currently, the classification accuracy of surface electromyography (sEMG) signals is high in literature, but the conventional recognition system may classify untrained movements or the trained movements of low reliability to one of its target classes by mistake. If such a system is used for prosthetic control, sometimes it may cause a disaster. A two-layer classifier that fuses the Gaussian mixture model (GMM) and k-nearest neighbor (kNN) in a sequential structure is proposed in this study. The proposed algorithm can reject the trained movements with low reliability and is efficient in rejecting the untrained movements, thus enhancing the reliability of the myoelectric control system. The results show that the proposed algorithm can produce 95.7% active accuracy in recognizing 12 trained movements and a 30.3% error rate for rejecting 12 untrained movements. When the movement number is six, the active accuracy for trained movements can reach 99.2%, and the error rate of untrained movement is only 17.4%, which is much better than previous studies. Therefore, the proposed classifier can accurately recognize the trained movements and reject untrained movement patterns effectively. myelectic signal; pattern recognition; machine learning; sEMG; reject option; GMM; kNN  
the untrained movements as one of the target movements by mistake [15], resulting in an unexpected activation of prostheses, which will significantly reduce the usability and reliability of the myoelectric control system [16]. It may also frustrate the users and make them refuse to use the device again. Hence, the rejection of untrained movements (novelty rejection), which is the second research question in this study, is also essential. As for prostheses control, it is believed that the error that causes accidental activations of the prostheses is more \"costly\" than those that cause a pause in motion [17]. A PR system with a rejection option to the sEMG signals with low similarities to training samples would reduce the potential risk due to uncertain decisions. Those sEMG signals can either be signals from trained movements or novel movements. The study aims to improve the reliability of the sEMG recognition system by increasing the accuracy of recognized samples and reducing the influence of untrained/unknown movement patterns [18] so that the system can be used for practical applications. An efficient two-layer classifier is proposed in this study. The proposed classifier predicts the test sample firstly by a time-efficient Gaussian mixture model (GMM) with a reject option (GMM-R) classifier. Then, the test sample will either be rejected or passed to the second-layer k-nearest neighbor (kNN) with a reject option (kNN-R) classifier based on the prediction result of the GMM-R classifier. Suppose the first layer classifier rejects the test sample. In that case, it is up to the second layer classifier to determine whether it gives a prediction or rejects the current decision. The advantage of the proposed classifier is that it takes advantage of GMM for its fast calculation and the kNN classifier for its high accuracy. So, it outperforms GMM and kNN in accuracy and is much more computationally efficient than kNN. With the proposed algorithm, objectives of this study can be achieved: (1) increasing the recognition accuracy of labeled samples while reducing their rejection rate and (2) enhancing the rejection rate of novel samples. Besides, the algorithm is computationally efficient for both model training and test sample recognition. The paper is organized as follows. A literature review on classification with reject options is introduced in Section 2. Section 3 describes the dataset used in this study, signal processing and feature extraction method, and the model evaluation method. The description of the proposed model is introduced in Section 4. Then, the experimental results are presented in Section 5. A discussion on proposed algorithms and comparison with other studies are conducted in Section 6.