## Appendix A Model Parameters
### Analysis of Pre-Processing and Post-Processing Modules  
In Table 11, we present the results with and without the pre-processing and/or the post-processing module. This evaluation was carried over the validation user set following the baseline obtained previously. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pre-Processing** & **Post-Processing** & \
\begin{tabular}{c} **Classification** \\ **Accuracy (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Recognition** \\ **Accuracy (\%)** \\ \end{tabular} \\ \hline No & No & \(96\pm 1.82\) & \(59.57\pm 10.33\) \\ \hline Yes & No & \(96.87\pm 2.3\) & \(64.35\pm 10.25\) \\ \hline No & Yes & \(96\pm 1.82\) & \(93.12\pm 5.08\) \\ \hline
**Yes** & **Yes** & \(\mathbf{96.87\pm 2.3}\) & \(\mathbf{93.25\pm 5.05}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Validation tests for hyper-parameters tuning. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hidden Units** & **Lambda** & **Epochs** & \
\begin{tabular}{c} **Classification** \\ **Accuracy (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Recognition** \\ **Accuracy (\%)** \\ \end{tabular} \\ \hline
100 & \(1\times 10^{-5}\) & 300 & \(96.95\pm 2.05\) & \(92.13\pm 5.32\) \\ \hline
250 & \(1\times 10^{-5}\) & 300 & \(97.41\pm 1.9\) & \(92.32\pm 5.48\) \\ \hline
500 & \(1\times 10^{-5}\) & 300 & \(97.36\pm 2.03\) & \(92.37\pm 6.17\) \\ \hline
100 & \(1\times 10^{-4}\) & 300 & \(97.13\pm 2.18\) & \(92.48\pm 5.06\) \\ \hline
100 & \(1\times 10^{-5}\) & 500 & \(96.64\pm 1.94\) & \(92.48\pm 4.88\) \\ \hline
500 & \(1\times 10^{-4}\) & 300 & \(97.31\pm 2.17\) & \(92.51\pm 6\) \\ \hline
250 & \(1\times 10^{-4}\) & 300 & \(97.36\pm 1.98\) & \(92.53\pm 5.33\) \\ \hline
100 & \(1\times 10^{-4}\) & 500 & \(96.87\pm 2.22\) & \(92.61\pm 4.35\) \\ \hline
250 & \(1\times 10^{-4}\) & 500 & \(97.13\pm 1.75\) & \(92.72\pm 5.22\) \\ \hline
250 & \(1\times 10^{-5}\) & 500 & \(96.85\pm 1.58\) & \(92.91\pm 5.36\) \\ \hline
500 & \(1\times 10^{-4}\) & 500 & \(96.64\pm 2.16\) & \(93.15\pm 5.28\) \\ \hline
**500** & \(1\times 10^{-5}\) & **500** & \(\mathbf{96.87\pm 2.3}\) & \(\mathbf{93.25\pm 5.05}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Confusion matrix of the proposed model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & **Targets** & & **Predictions Count** \\ \cline{2-7}  & **waveIn** & **waveOut** & **Fist** & **fingersSpread** & **doubleTap** & **relax** & **(Precision \%)** \\ \hline waveIn & **747** & 1 & 2 & 0 & 1 & 8 & **759** \\ \hline waveOut & 2 & **746** & 0 & 8 & 4 & 11 & **771** \\ \hline fist & 1 & 0 & **741** & 6 & 3 & 12 & **763** \\  & & & & & & **97.12\%** \\ \hline fingersSpread & 0 & 3 & 1 & **735** & 3 & 28 & **770** \\  & & & & & & **95.45\%** \\ \hline doubleTap & 0 & 0 & 6 & 1 & **739** & 21 & **767** \\  & & & & & & **96.35\%** \\ \hline relax & 0 & 0 & 0 & 0 & 0 & **70** \\  & & & & & & **100\%** \\ \hline
**Targets Count** & **750** & **750** & **750** & **750** & **750** & **150** & **3900** \\
**(Sensitivity \%)** & **99.6\%** & **99.47\%** & **98.8\%** & **98\%** & **98.53\%** & **46.67\%** & **96.87\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Validation tests for hyper-parameters tuning. In Table A3, it can be observed that classification results are similar in these tests (around 96%, see Tables A4-A6). On the other hand, recognition accuracy results show the effects and importance of the post-processing module increasing performance from 64.35% to 93.25%. With these results, we justify the pre and post-processing modules used in this hand gesture recognition model.