# Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
## 2 Literature Review  
Classification with a rejection option has been applied in many areas, such as optical character recognition [19; 20], medical diagnosis [21], and engineering [22]. Despite the difference in their application background, the algorithms have something in common. Though ambiguity rejection and novelty rejection are two different rejection types, and they are both concerned in this study, the algorithms developed for one can be applied for another in most cases. So, we do not differentiate them on purpose when conducting the literature review but focus more on the algorithm itself. A common algorithm used in related research is linear discriminant analysis (LDA). The authors of [17; 23] proposed a multiple binary LDA classifier with \"1-vs-all\" and \"1-vs-1\" topology for the rejection of motions when the multiple binary classifiers cannot reach an agreement. Though it effectively limits the number of active movements, [23] could be problematic as the increase of motions and [17] suffer from too frequent rejections. The authors of [24] proposed a confidence-based rejection scheme based on the outputs of LDA. It generates a confidence score for each observation, and the observation with a confidence score lower than the predefined threshold will be rejected. A similar method is followed in [25], except that the class-specific threshold is automatically determined using receiver operating characteristic (ROC) curves. The reject option can also be used with neural networks. The researchers of [26] developed a method for defining a reject option by estimating the classification reliability as measured by a reliability evaluator and applied the method to three neural network paradigms. A post-processing algorithm for a multiple layer perceptron (MLP) neural network was proposed in [27] to detect and remove possible misclassifications from LDA. Deep neural networks (DNNs) have also been used for novelty detection in [28; 29]. Although these algorithms have demonstrated their effectiveness through experimental results, the training of neural networks is time consuming. Previous publications have shown that combing classifiers will improve the accuracy of individual classifiers; refs. [30; 31; 32] analyzed the error-reject trade-off in three linearly combined classifiers. The experiments showed that the linear combination of the classifiers might improve the error-reject trade-off of the individual classifiers; still, the improvement depends on the type of combination and level of rejection rate. An adaptive hybrid classifier consisting of one-class support vector descriptors (SVDDs) and a multi-class LDA was proposed in [33] to reduce the impact of interference on myoelectric pattern recognition. The LDA classifier will be adopted for classification if the test observation is not detected as an outlier by SVDD. A similar strategy has been adopted in [18]. In both studies, the classification accuracy of the identified active movements is mainly dependent on the selected classifier, and SVDD works as an outlier filter [12; 34]. Besides, SVDD may result in a large run-time complexity as the increase of training set, which may introduce time delays to the control system. Unlike studies above, a training strategy was proposed in [16] to categorize all the unwanted movements (UMs) into a new movement class (UMs-combined motion class) to reduce the impact of unwanted movements. Since the LDA classifier is trained by the sEMG signals from both target classes and the UMS-combined motion class, the usability of the proposed strategy is limited in practical applications because the unwanted movements are usually unknown in advance. The studies mentioned above provide a good reference for developing classifiers with reject options. It can be noticed that many related studies developed algorithms based on an LDA classifier. Though an LDA classifier can be extended to have a rejection option conveniently and is computationally efficient, its classification accuracy is limited compared with other classifiers [35; 36].