## 5 Discussion  
Differences between a classifier and a class-modelling classifier such as PLS-ECOC were stated in the Introduction and State-of-the-Art Approaches Sections. To summarize, the main difference is that a class-modelling classifier can assign one gesture to none or more than one class. This feature combined with the PLS-box was used in this work to implement a fault-tolerant classifier that can detect _errors_, increasing the robustness of the whole system. This feature could not be achieved with previous classifiers used in sEMG GR applications. To fully evaluate the extra information provided by a class-modelling classifier, the DMCEN [46] metric was used as described in Section 3.6. The usual metrics (accuracy, precision, or F1 score) will fall short because they assume that each object can belong to only one class. However, for the sake of comparison with other classifiers in the sEMG GR field, it seems reasonable to express the results also in the usual metric format: accuracy, precision, and F1 score. To this purpose, the _errors_ (gestures assigned to none or two or more classes) were assigned to their most probable class. Note that with this transformation, the class-modelling algorithm loses its fault-tolerant capability. Considered PLS-ECOC as a classification method, the confusion matrix in training \(\mathbf{N}_{TR}=(\mathrm{n}_{\mathrm{ii}})\), is diagonal, with \(\mathrm{n}_{\mathrm{ii}}=7\) and \(\mathrm{n}_{\mathrm{ij}}=0\) if \(\mathrm{i}\
eq\mathrm{j}\). Therefore, accuracy, precision, and F1 are all equal to 100%. In prediction, with the test set, the confusion matrix is shown in Table 4. The accuracy, precision, and F1 values are 87.5, 91.87, and 86.34%, respectively. They have been calculated in accordance with ref. [63] to maintain consistency with the definitions of sensitivity and specificity given in the introduction of Section 3. However, in the field of sEMGs, there is no agreement on the definition of accuracy, and this ambiguity makes it difficult to compare results. Section 3.5 of ref. [33] is devoted to this question. The authors find that one work of the 56 studied defines \"the recognition accuracy\" (a gesture is considered a true positive, i.e., the gesture is recognized correctly when the model determines what gesture was performed and when this gesture was performed by a person). Eleven other papers use the concept of \"classification accuracy\" because they only took into consideration what gesture was performed by a person as a true positive, and the remaining papers do not show what they consider a true positive. This ambiguity is transferred to the calculation of accuracy. In our case, both definitions coincide with each other and with our calculation because the rest position (no gesture, in other papers) has been considered as a class in the model. Other definitions of accuracy used in the analysis of sEMG signals are the ratio of correctly predicted gestures (true positives plus true negatives) to the total gestures [67; 68], obtaining 98.44% with our data. In [33], accuracy is also defined as the arithmetic mean of sensitivity and specificity, which, in our case, would be 87.5%. In any case, the accuracy of PLS-ECOC transformed into a classifier is in the range of values found in the literature described in Section 2. Specifically, using the means and standard deviations obtained from the accuracies reported in references [34; 35; 14; 33] and