## 2 State-of-the-Art Approaches
This possibility in discriminant classifiers would not be available, and the only possible action would be to add more data to the dataset with the objective of improving its accuracy. In the field of gesture recognition using EMG signals, class modelling has never been used. In the literature reviewed, only gesture classification has been performed. Publications powered by pattern recognition methods have been reviewed in references [33; 34; 35]. The most commonly used are SVM [36; 37] and NNs [38]. After the signal acquisition, among all the modules that compose a gesture recognition system, perhaps the most important and critical ones are feature extraction and the classification algorithm. In this regard, the algorithms and results of these references are summarized below. The review by [PERSON] et al. [35] of works from 1999 to 2013 shows that the classifiers used are linear discriminant analysis, SVM, adaptive network-based fuzzy inference system, NNs, and fuzzy logic. The range of accuracy is [73.00, 98.87] with a mean of 90.60% and a standard deviation of 7.06%. Moreover, [PERSON] et al. 's review [33] from 2013 to 2019 showed that the classifiers used are support vector machines, feedforward neural networks, linear discriminant analysis, convolutional neural networks with several variants, k-nearest neighbours coupled with several other procedures binary tree-support vector machines, vector autoregressive hierarchical hidden Markov models, Gaussian mixture models and hidden Markov models, quadratic discriminant analysis, fuzzy logic, recurrent neural networks, generalized regression neural networks, and one vs. one classifier. Among them, the most commonly used machine learning algorithms are support vector machines, feedforward neural networks, and linear discriminant analysis. The accuracy of the reviewed publications ranges from 71.00% to 99.78% with a mean of 91.57% and a standard deviation of 6.35%. Similar accuracy is reported (63.74% to 99.23% with a mean of 89% and a standard deviation of 10.11%) by [PERSON] and [PERSON]'s [34] revision between 2017 and 2021. A more recent comparison evaluated five machine learning techniques in classifying daily gestures [10]. It reported an accuracy ranging from 47.7% to 83.6% with a mean value of 66.54% and a standard deviation of 14.95%. This revision included SVN, a random forest, a decision tree-based algorithm, a convolutional neural network, and a recurrent neural network. The analysis of the abovementioned reviews also shows that in order to capture and describe the complexity and variability of sEMG signals, it is considered necessary to handle \"deep learning\" methods. This trend can be observed in related fields such as gesture recognition based on electroencephalogram (EEG) signals. In [PERSON] et al. 's revision [38], a taxonomy of deep learning models was made according to their functionality in different scenarios of brain activity. After a very extensive description of applications, the authors indicated that it is still very challenging to produce classification results in real-time. In [39], GR performance was improved by using a graph sequence neural network applied to an HMI problem. In [40], six features were obtained using the correlation and canonical correlation analysis between filtered signals; after that, a selection was conducted. However, these advances have not yet been translated into their equivalent sEMG signals. [PERSON] and [PERSON] [41] made a very systematic analysis of the use of deep learning for classifying sEMG signals and define two categories of computational procedures:  
* Feature engineering. Under this heading, the methods to find the best combination of features in a specific problem are grouped. They are parallel computing procedures, either native or classics parallelized. Then, conventional classifiers, such as support vector machines, linear discriminant analysis, k-nearest neighbours, random forests, multilayer perceptron neural networks, etc., are applied. * Feature learning.