## 4 Results
### Performance Evaluation on Test Data  
Figure 6 shows the confusion matrices based on the best-performed model (InceptionV4) on the test data. The confusion matrices for ConvNeXt, EdgeNeXt, EfficientNet, NanoViT, and MobileNetV3 are presented in the Appendix C in Figures A2-A6, respec \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Test Scenario** & **Precision** & **Recall** & **F1-Score** & **Specificity** & **Accuracy** \\ \hline \multirow{4}{*}{EgoGesture} & Test data & 0.80 & 0.78 & 0.76 & 0.95 & 0.80 \\  & Indoor & 0.78 & 0.75 & 0.76 & 0.93 & 0.75 \\  & Outdoor & 0.71 & 0.69 & 0.70 & 0.90 & 0.69 \\  & Cross-hands & 0.68 & 0.66 & 0.67 & 0.88 & 0.67 \\  & All scenarios & 0.74 & 0.72 & 0.73 & 0.91 & 0.72 \\ \hline \multirow{4}{*}{NVGesture} & Test data & 0.71 & 0.68 & 0.67 & 0.96 & 0.71 \\  & Indoor & 0.69 & 0.67 & 0.68 & 0.94 & 0.67 \\  & Outdoor & 0.63 & 0.61 & 0.62 & 0.90 & 0.61 \\  & Cross-hands & 0.60 & 0.58 & 0.59 & 0.88 & 0.58 \\  & All scenarios & 0.65 & 0.63 & 0.64 & 0.90 & 0.63 \\ \hline \multirow{4}{*}{Jesture} & Test data & 0.64 & 0.60 & 0.59 & 0.90 & 0.63 \\  & Indoor & 0.60 & 0.58 & 0.59 & 0.88 & 0.58 \\  & Outdoor & 0.55 & 0.53 & 0.54 & 0.85 & 0.53 \\  & Cross-hands & 0.60 & 0.58 & 0.59 & 0.87 & 0.58 \\  & All scenarios & 0.61 & 0.59 & 0.60 & 0.88 & 0.59 \\ \hline \multirow{4}{*}{SignLanguage} & Test data & 0.94 & 0.94 & 0.93 & 0.98 & 0.94 \\  & Indoor & 0.91 & 0.90 & 0.90 & 0.96 & 0.90 \\ \cline{1-1}  & Outdoor & 0.87 & 0.86 & 0.86 & 0.94 & 0.86 \\ \cline{1-1}  & Cross-hands & 0.92 & 0.91 & 0.91 & 0.97 & 0.91 \\ \cline{1-1}  & All scenarios & 0.91 & 0.89 & 0.90 & 0.96 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Detailed performance metrics for the NanoViT model, evaluated on test data and assessed through subjective testing scenarios. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Test Scenario** & **Precision** & **Recall** & **F1-Score** & **Specificity** & **Accuracy** \\ \hline \multirow{4}{*}{EgoGesture} & Test data & 0.91 & 0.90 & 0.90 & 0.98 & 0.90 \\  & Indoor & 0.89 & 0.88 & 0.88 & 0.95 & 0.88 \\  & Outdoor & 0.82 & 0.81 & 0.81 & 0.94 & 0.83 \\  & Cross-hands & 0.75 & 0.73 & 0.72 & 0.93 & 0.76 \\  & All scenarios & 0.84 & 0.83 & 0.83 & 0.95 & 0.85 \\ \hline \multirow{4}{*}{NVGesture} & Test data & 0.78 & 0.77 & 0.77 & 0.97 & 0.77 \\  & Indoor & 0.76 & 0.75 & 0.75 & 0.95 & 0.76 \\ \cline{1-1}  & Outdoor & 0.67 & 0.66 & 0.66 & 0.96 & 0.68 \\ \cline{1-1}  & Cross-hands & 0.66 & 0.63 & 0.62 & 0.93 & 0.66 \\ \cline{1-1}  & All scenarios & 0.71 & 0.70 & 0.70 & 0.96 & 0.72 \\ \hline \multirow{4}{*}{Jesture} & Test data & 0.73 & 0.73 & 0.73 & 0.95 & 0.73 \\ \cline{1-1}  & Indoor & 0.71 & 0.70 & 0.70 & 0.90 & 0.71 \\ \cline{1-1}  & Outdoor & 0.60 & 0.59 & 0.59 & 0.92 & 0.60 \\ \cline{1-1}  & Cross-hands & 0.58 & 0.55 & 0.54 & 0.87 & 0.57 \\ \cline{1-1}  & All scenarios & 0.65 & 0.64 & 0.64 & 0.90 & 0.65 \\ \hline \multirow{4}{*}{SignLanguage} & Test data & 0.94 & 0.93 & 0.94 & 0.98 & 0.94 \\ \cline{1-1}  & Indoor & 0.90 & 0.90 & 0.90 & 0.98 & 0.90 \\ \cline{1-1}  & Outdoor & 0.88 & 0.87 & 0.87 & 0.97 & 0.88 \\ \cline{1-1}  & Cross-hands & 0.91 & 0.90 & 0.90 & 0.97 & 0.91 \\ \cline{1-1}  & All scenarios & 0.91 & 0.91 & 0.91 & 0.97 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Detailed performance metrics for the MobileNetV3 model, evaluated on test data and assessed through subjective testing scenarios. tively. Among these confusion matrices, it is shown that the sign-language dataset was most successful in training the model well and the least successful was the Jester dataset. As shown in Figure 6d the model trained with the sign-language dataset has accurately detected most of the true positives and true negatives as actual true positives and actual true negatives. Table 2 is also based on these results. Based on the test data, the performance evaluation metrics are reported and can be referred to in Tables 3-8 under the rows titled \"test data\".