## 4 Results
To understand the benefits of modelling sEMG signals as an alternative to usual classifiers, the assignation matrix, Table 3, of the test dataset \(\mathbf{X}_{TS}\) is shown. This matrix was obtained by applying the \(PLS_{OP}\) model and decoding it. Each row of the matrix represents one sEMG signal from \(\mathbf{X}_{TS}\) and is composed of a vector of zeros and ones indicating the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**True Class**} & \multicolumn{8}{c}{**Model of Class**} \\ \cline{2-9}  & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) & \(\mathbf{C_{4}}\) & \(\mathbf{C_{5}}\) & \(\mathbf{C_{6}}\) & \(\mathbf{C_{7}}\) & \(\mathbf{C_{8}}\) \\ \hline \(C_{1}\) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{2}\) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{2}\) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{3}\) & 0.86 * & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{4}\) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{5}\) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{6}\) & 1 & 1 & 1 & 1 & 0.71 ** & 1 & 1 & 1 \\ \(C_{7}\) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{8}\) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular} (*) Sensitivity of \(C_{3}\) class model. (**) Specificity of \(C_{3}\) class model against \(\mathrm{C}_{8}\). (***) Specificity of \(C_{5}\) class model against \(\mathrm{C}_{6}\).  
\end{table} 
Table 1: Sensitivity and specificity matrix \(\mathbf{S}_{TR}\) for the test dataset \(\mathbf{X}_{TR}\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**True Class**} & \multicolumn{8}{c}{**Model of Class**} \\ \cline{2-9}  & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) & \(\mathbf{C_{4}}\) & \(\mathbf{C_{5}}\) & \(\mathbf{C_{6}}\) & \(\mathbf{C_{7}}\) & \(\mathbf{C_{8}}\) \\ \hline \(C_{1}\) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{2}\) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{3}\) & 1 & 1 & 0.67 * & 1 & 1 & 1 & 1 & 1 \\ \(C_{4}\) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{5}\) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{6}\) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{7}\) & 1 & 1 & 0.33 ** & 1 & 1 & 1 & 1 & 1 \\ \(C_{8}\) & 1 & 1 & 0.33 ** & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular} (*) Specificity of \(C_{3}\) class model. (**) Specificity of \(C_{3}\) class model against \(\mathrm{C}_{8}\). (***) Specificity of \(C_{5}\) class model against \(\mathrm{C}_{6}\).  
\end{table} 
Table 2: Sensitivity and specificity matrix \(\mathbf{S}_{TS}\) for the test dataset \(\mathbf{X}_{TS}\). class assignment made by the model. Each signal sEMG belonging to test dataset (\(\mathbf{X}_{TS}\)) is a row of Table 3, and each 1 means that the class model, the column, accepted the signal. In this analysis, an _error_ is defined as a signal that belongs to a number of gestures different from one, a _failure_ is defined as a signal that is assigned solely to the wrong class, and a success is defined as a signal that is correctly assigned to its class. For example, Row 7 of Table 3 shows an sEMG signal from the \(C_{3}\) gesture class that was not assigned to any class, resulting in an _error_ and reducing the sensitivity of the \(C_{3}\) class model, as indicated by the (*) label in Table 2  
Rows 17 and 18 show two signals from the \(C_{6}\) class that have been assigned to two classes, the correct one (\(C_{6}\)) and an incorrect one (\(C_{5}\)). This results in two _errors_, but the \(C_{6}\) class model sensitivity is not affected. However, incorrect assignment of the signals to \(C_{5}\) reduces the specificity of the \(C_{5}\) class model against the \(C_{6}\) class, as indicated by the (***) label in Table 2.  
Similarly, rows 23 and 24 show two signals that are correctly assigned to the \(C_{8}\) class but are also incorrectly assigned to the \(C_{3}\) class, which reduces the specificity of the \(C_{3}\) class model against the \(C_{8}\) class and adding two _errors_ to the overall results.