## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**True Class**} & \multicolumn{6}{c}{**Model of Class**} \\ \cline{2-9}  & \(C_{1}\) & \(C_{2}\) & \(C_{3}\) & \(C_{4}\) & \(C_{5}\) & \(C_{6}\) & \(C_{7}\) & \(C_{8}\) \\ \hline \(C_{1}\) & 3 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \(C_{2}\) & 0 & 3 & 0 & 0 & 0 & 0 & 0 & 0 \\ \(C_{3}\) & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 1 \\ \(C_{4}\) & 0 & 0 & 0 & 3 & 0 & 0 & 0 & 0 \\ \(C_{5}\) & 0 & 0 & 0 & 0 & 3 & 0 & 0 & 0 \\ \(C_{6}\) & 0 & 0 & 0 & 0 & 2 & 1 & 0 & 0 \\ \(C_{7}\) & 0 & 0 & 0 & 0 & 0 & 0 & 3 & 0 \\ \(C_{8}\) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix \(\mathbf{N}_{TS}\) for the test dataset \(\mathbf{X}_{TS}\). assuming that the data come from a normal distribution, the tolerance limits state that we can be 95.0% confident that 99.0% of the distribution lies above 73.62, 52.76, 63.40, and 71.92%, respectively. The value obtained, 87.5%, with PLS-ECOC transformed into a classifier is inside all four tolerance intervals. Another important aspect of the proposed class-model is that it does not require human intervention in adjusting it. Most approaches in the sEMG GR field require the selection of features to make the classifier work or to adjust the parameters of the classifier. By contrast, the proposed method is totally automated, simultaneously adjusting feature extraction, dimensionality reduction in the _nvl_ latent variables, and classification performance using the DMCEN index. It only needs a labelled dataset to work with. This approach should provide better flexibility, reducing design time. Another advantage from the designer's point of view is the information that the PLS-ECOC provides; the sensitivity and specificity matrix \(\mathbf{S}\) can give important insights on which gestures are being confused, providing a useful debug tool to fix or improve the overall system design. For example, it can highlight which electrodes should be placed differently or that some gestures cannot be used as commands for the system. The last part of this discussion will focus on the applicability of the presented method for a wearable, real-time application. Three metrics are evaluated: the size of the model, latency, and scalability. The size of the \(PLS_{OP}\) model is extremely important for wearable devices, as it directly impacts performance and limits usability. Any lineal model has a parameter size of \(nvc\times nc\times c\) where \(nvc\) is the number of voltage values recorded, \(nc\) is the number of channels, and \(c\) is the codeword length. The computation required is also proportional to the size of the model because obtaining the classification value will require one multiplication and one addition per parameter. This size is comparable with the SVM algorithm. Translated into memory parameters, it will occupy 664 kB, a size that could not be fitted into the ram memory of a low-power MCU. However, the current implementation uses a 3 s gesture as an input. So, the size will fold by 30 in a real case scenario where windowing of the order of the hundredth of milliseconds is used. Latency, another key parameter, is composed of the time to acquire one gesture to be evaluated (3 s) and the time to process it. Being a linear model, the processing time in an MCU will be low because it is only composed of a multiplication and an addition per parameter. The last parameter is scalability. PLS-ECOC codification makes the size of the model increase logarithmically (\(c\cong 10\log_{2}K\)) compared to SVM usual encoding, one vs. one, which increases quadratically (\(c=K(K-1)/2\)), being, in both cases, \(K\), the number of classes, providing a good base for more complex HMI systems that will require a large number of gestures to be functional. All these characteristics should be tested by implementing PLS-ECOC in a real embedded device and evaluating its performance.