## 6 Results and Discussion
### Performance Metrics for Three Sessions  
Building upon our initial exploration utilizing data from one session of the GRABMyo dataset, this section delves into the outcomes derived from employing data spanning three sessions. Notably, our preliminary analysis revealed that the LDA-SVM model achieved the highest accuracy when trained and evaluated on data from a single session. Leveraging these insights, we aim to ascertain whether employing the entire dataset, encompassing multiple sessions, will yield superior results, thus harnessing the potential benefits of a larger and more comprehensive database. Figure 26 illustrates the methodology for processing the three sessions and consolidating them to be utilized for machine learning. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation Metrics** & \multicolumn{5}{c}{**Classes (Five Hand Gestures)**} & \multicolumn{2}{c}{**Macro Average**} \\ \hline \hline  & \multicolumn{5}{c}{**LDA-K-Nearest Neighbor**} \\ \hline True Positive & 14 & 29 & 24 & 33 & 25 & 25 \\ False Positive & 10 & 14 & 24 & 20 & 22 & 18 \\ False Negative & 29 & 14 & 19 & 10 & 18 & 18 \\ True Negative & 162 & 158 & 148 & 152 & 150 & 154 \\ Precision & 0.58333 & 0.67442 & 0.5 & 0.62264 & 0.53191 & 0.58246 \\ Sensitivity & 0.32558 & 0.67442 & 0.55814 & 0.76744 & 0.5814 & 0.5814 \\ Accuracy & 0.8186 & 0.86977 & 0.8 & 0.86047 & 0.81395 & 0.83256 \\ F-measure & 0.41791 & 0.67442 & 0.52747 & 0.6875 & 0.55556 & 0.57257 \\ \hline \multicolumn{5}{c}{**LDA-Naïve Bayes**} \\ \hline True Positive & 17 & 22 & 10 & 27 & 13 & 17.8 \\ False Positive & 44 & 12 & 25 & 34 & 11 & 25.2 \\ False Negative & 26 & 21 & 33 & 16 & 30 & 25.2 \\ True Negative & 128 & 160 & 147 & 138 & 161 & 146.8 \\ Precision & 0.27869 & 0.64706 & 0.28571 & 0.44262 & 0.54167 & 0.43915 \\ Sensitivity & 0.39535 & 0.51163 & 0.23256 & 0.62791 & 0.30233 & 0.41395 \\ Accuracy & 0.67442 & 0.84651 & 0.73023 & 0.76744 & 0.8093 & 0.76558 \\ F-measure & 0.32692 & 0.57143 & 0.25641 & 0.51923 & 0.38806 & 0.41241 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: LDA performance metrics for machine learning models, KNN, NB, and the SVM, using data from one session. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation Metrics** & \multicolumn{5}{c}{**Classes (Five Hand Gestures)**} & \multicolumn{2}{c}{**Macro Average**} \\ \hline \hline  & \multicolumn{5}{c}{**PCA-K-Nearest Neighbor**} \\ \hline True Positive & 6 & 24 & 18 & 22 & 19 & 17.8 \\ False Positive & 13 & 23 & 40 & 29 & 21 & 25.2 \\ False Negative & 37 & 19 & 25 & 21 & 24 & 25.2 \\ True Negative & 159 & 149 & 132 & 143 & 151 & 146.8 \\ Precision & 0.31579 & 0.51064 & 0.31034 & 0.43137 & 0.475 & 0.40863 \\ Sensitivity & 0.13953 & 0.55814 & 0.4186 & 0.51163 & 0.44186 & 0.41395 \\ Accuracy & 0.76744 & 0.80465 & 0.69767 & 0.76744 & 0.7907 & 0.76558 \\ F-measure & 0.19355 & 0.53333 & 0.35644 & 0.46809 & 0.45783 & 0.40185 \\ \hline \multicolumn{5}{c}{**PCA-Naïve Bayes**} \\ \hline True Positive & 12 & 15 & 12 & 26 & 18 & 16.6 \\ False Positive & 28 & 11 & 36 & 36 & 21 & 26.4 \\ False Negative & 31 & 28 & 31 & 17 & 25 & 26.4 \\ True Negative & 144 & 161 & 136 & 136 & 151 & 145.6 \\ Precision & 0.3 & 0.57692 & 0.25 & 0.41935 & 0.46154 & 0.40156 \\ Sensitivity & 0.27907 & 0.34884 & 0.27907 & 0.60465 & 0.4186 & 0.38605 \\ Accuracy & 0.72558 & 0.8186 & 0.68837 & 0.75349 & 0.78605 & 0.75442 \\ F-measure & 0.28916 & 0.43478 & 0.26374 & 0.49524 & 0.43902 & 0.38439 \\ \hline \multicolumn{5}{c}{**PCA-Support Vector Machine**} \\ \hline True Positive & 18 & 28 & 20 & 28 & 23 & 23.4 \\ False Positive & 24 & 20 & 30 & 11 & 13 & 19.6 \\ False Negative & 25 & 15 & 23 & 15 & 20 & 19.6 \\ True Negative & 148 & 152 & 142 & 161 & 159 & 152.4 \\ Precision & 0.42857 & 0.58333 & 0.4 & 0.71795 & 0.63889 & 0.55375 \\ Sensitivity & 0.4186 & 0.65116 & 0.46512 & 0.65116 & 0.53488 & 0.54419 \\ Accuracy & 0.77209 & 0.83721 & 0.75349 & 0.87907 & 0.84651 & 0.81767 \\ F-measure & 0.42353 & 0.61538 & 0.43011 & 0.68293 & 0.58228 & 0.54685 \\ \hline \hline \end{tabular}
\end{table}