# Fall Recognition Based on Time-Level Decision Fusion Classification
## 4 Experimental Results
### Dataset and Experimental Setup
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fusion Method**} & \multirow{2}{*}{
\begin{tabular}{c} **Optimal Number of** \\ **Classifiers** \\ \end{tabular} } & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Data Label**} & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**Accuracy (\%)**} \\ \cline{5-6} \cline{8-8}  & & & & & Fall & 96.73 & 92.94 \\ \cline{5-6} \cline{8-8} \multirow{2}{*}{Baseline} & \multirow{2}{*}{-} & \multirow{2}{*}{ViTPose} & Normal & 93.16 & 96.84 \\ \cline{5-6} \cline{8-8}  & & & & Fall & 93.65 & 89.61 \\ \cline{5-6} \cline{8-8}  & & & & & Normal & 89.96 & 93.87 \\ \hline \multirow{4}{*}{Probability} & \multirow{4}{*}{10} & \multirow{4}{*}{ViTPose} & Fall & 97.90 & 91.37 \\ \cline{5-6} \cline{8-8}  & & & & Normal & 91.85 & 98.02 \\ \cline{5-6} \cline{8-8}  & & & & Normal & 91.36 & 94.07 \\ \hline \multirow{4}{*}{Average} & \multirow{4}{*}{5} & \multirow{4}{*}{ViTPose} & Fall & 97.26 & 90.59 \\ \cline{5-6} \cline{8-8}  & & & Normal & 91.13 & 97.43 \\ \cline{5-6} \cline{8-8}  & & & & & Normal & 91.87 \\ \cline{5-6} \cline{8-8}  & & & Normal & 90.32 & 94.07 \\ \hline \multirow{4}{*}{Max} & \multirow{4}{*}{5} & \multirow{4}{*}{ViTPose} & Fall & 97.46 & 90.39 \\ \cline{5-6} \cline{8-8}  & & & Normal & 90.98 & 97.63 \\ \cline{5-6} \cline{8-8}  & & & & Fall & 93.78 & 88.63 \\ \cline{5-6} \cline{8-8}  & & & Normal & 89.14 & 94.07 \\ \hline \multirow{4}{*}{Voting} & \multirow{4}{*}{5} & \multirow{4}{*}{ViTPose} & Fall & 98.28 & 89.80 \\ \cline{5-6} \cline{8-8}  & & & Normal & 90.55 & 98.42 \\ \cline{5-6} \cline{8-8}  & & & & Fall & 93.78 & 88.63 \\ \cline{5-6} \cline{8-8}  & & & Normal & 89.14 & 94.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of fall detection using YOLOv7 with the optimal number of classifiers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fusion Method**} & \multirow{2}{*}{
\begin{tabular}{c} **Optimal Number of** \\ **Classifiers** \\ \end{tabular} } & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Data Label**} & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**Accuracy (\%)**} \\ \cline{5-6} \cline{8-8}  & & & & Fall & 97.49 & 91.57 \\ \cline{5-6} \cline{8-8}  & & & Normal & 91.99 & 97.63 \\ \cline{5-6} \cline{8-8}  & & & & Fall & 93.65 & 89.61 \\ \cline{5-6} \cline{8-8}  & & & Normal & 89.96 & 93.87 \\ \hline \multirow{4}{*}{Probability} & \multirow{4}{*}{10} & \multirow{4}{*}{ViTPose} & Fall & 97.90 & 91.37 \\ \cline{5-6} \cline{8-8}  & & & Normal & 91.85 & 98.02 \\ \cline{5-6} \cline{8-8}  & & & & Fall & 93.94 & 91.18 \\ \cline{5-6} \cline{8-8}  & & & Normal & 91.36 & 94.07 \\ \hline \multirow{4}{*}{Average} & \multirow{4}{*}{5} & \multirow{4}{*}{ViTPose} & Fall & 97.26 & 90.59 \\ \cline{5-6} \cline{8-8}  & & & Normal & 91.13 & 97.43 \\ \cline{5-6} \cline{8-8}  & & & & Normal & 93.87 & 90.00 \\ \cline{5-6} \cline{8-8}  & & & Normal & 90.32 & 94.07 \\ \hline \multirow{4}{*}{Max} & \multirow{4}{*}{5} & \multirow{4}{*}{ViTPose} & Fall & 97.46 & 90.39 \\ \cline{5-6} \cline{8-8}  & & & Normal & 90.98 & 97.63 \\ \cline{5-6} \cline{8-8}  & & & Fall & 93.78 & 88.63 \\ \cline{5-6} \cline{8-8}  & & & Normal & 89.14 & 94.07 \\ \hline \multirow{4}{*}{Voting} & \multirow{4}{*}{5} & \multirow{4}{*}{ViTPose} & Fall & 98.28 & 89.80 \\ \cline{5-6} \cline{8-8}  & & & Normal & 90.55 & 98.42 \\ \cline{5-6} \cline{8-8}  & & & Fall & 93.78 & 88.63 \\ \cline{5-6} \cline{8-8}  & & & Normal & 89.14 & 94.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of fall detection using YOLOv3 with the optimal number of classifiers.