## 2 State-of-the-Art Approaches  
In gesture recognition using sEMG signals, computational techniques from machine learning (ML) are employed to assign a recorded signal to a gesture class. Specifically, a matrix \(\mathbf{X}\) of sEMG signals with \(N\) rows corresponds to \(N\) gestures grouped into \(K\) classes (\(C_{1},\ \ldots,\ C_{K}\)). There are two general approaches to addressing this issue: discriminant techniques and class modelling. The first one is a purely discriminative approach. In the training phase, the ML technique constructs a mathematical model and decision rule to assign each gesture class a subset \(\wp_{k},\ k=1,\ldots,K\) of the signal space in such a way that they are disjoint, and their union is the total. As a result, in the prediction phase, a new signal will necessarily be assigned to one of the classes and only one. These types of techniques are commonly known in pattern recognition as discriminant or classification techniques. The performance of a discriminant model is measured with the expected classification accuracy, namely, the percentage of correct decisions in prediction or variations of this metric. Examples of common purely discriminant methods include linear or quadratic discriminant analysis (LDA, QDA [18]), regularized discriminant analysis (RDA) [19], partial least squares discriminant analysis (PLS-DA) [20], classification and regression trees (CART) [21], or support vector machines (SVM) originally developed for two-class classification [22], generalized to the multiclass situation [23], and the case of unlabelled data [24]. Neural networks (NNs) have also been widely developed and improved since their inception [25], with the backpropagation algorithm [26] allowing for computational feasibility. NNs offer flexibility, allowing them to adapt their structure to improve classification accuracy. For example, the convolutional neural networks (CNNs) proposed in reference [27] have been applied to handwritten digit classification. The second approach for GR using EMG signals is known as the class-modelling technique. It also builds \(K\) subsets, named K-class models, which are represented by \(\wp_{k},\ k=1,\ldots,K\), but they are not necessarily digits, and their union may not cover the entire signal space. As a result, an object(in this case an sEMG signal) can belong to one or more classes (gestures) or none at all. To evaluate the performance of the class-modelling technique, the sensitivity and specificity of each class model are measured. In this context, the sensitivity of a class model refers to its ability to correctly identify its corresponding gesture (usually measured as the rate of correctly classified objects within the class model, \(\wp_{K}\)), while its specificity refers to its ability to correctly reject objects that do not belong to the class model (measured as the rate of correctly classified objects outside the class model). Among the class-modelling methods, some stand out: Soft independent models of class analogy (SIMCA) [28] have been widely used for product authentication, quality assurance, fraud detection, unequal class models or unequal dispersed classes (UNEQ) [29], or an adaptation of SVM [30]. Two-class modelling using PLS can be seen in reference [31] and for K-classes in [32]. The advantage over the usual class discriminant classifiers used in sEMG gesture recognition problems and the proposed class modelling approach is that it enables the system to be fault tolerant. This is because the class modelling approach does not limit the answer to a unique label. Another advantage is the extra information provided by the sensitivity and specificity of each class model that allows the engineer to adapt the design by including extra channels to detect the difference between the confused gestures or to remove certain gestures if they are being problematic prior to the deployment of the system.