# 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
**Fusion Method** &
\begin{tabular}{c} **Optimal Number of** \\ **Classifiers** \\ \end{tabular} & **Model** & **Data Label** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{Probability} & \multirow{4}{*}{5} & \multirow{4}{*}{ViTPose} & Fall & 97.15 & 93.53 & \multirow{4}{*}{95.37} \\ \cline{3-3}  & & Normal & 93.71 & 97.23 & & \\ \cline{3-3}  & & AlphaPose & Fall & 95.69 & 91.37 & \multirow{4}{*}{93.60} \\ \cline{3-3}  & & Normal & 91.68 & 95.85 & & \\ \hline \multirow{4}{*}{Average} & \multirow{4}{*}{15} & \multirow{4}{*}{ViTPose} & Fall & 91.96 & 98.12 & \multirow{4}{*}{95.08} \\ \cline{3-3}  & & Normal & 98.22 & 92.38 & & \\ \cline{3-3}  & & AlphaPose & Fall & 88.82 & 96.38 & \multirow{4}{*}{92.72} \\ \cline{3-3}  & & Normal & 96.64 & 89.56 & & \\ \hline \multirow{4}{*}{Max} & \multirow{4}{*}{10} & \multirow{4}{*}{ViTPose} & Fall & 91.76 & 97.70 & \multirow{4}{*}{94.78} \\ \cline{3-3}  & & Normal & 97.83 & 92.18 & & \\ \cline{3-3}  & & AlphaPose & Fall & 89.22 & 96.19 & \multirow{4}{*}{92.81} \\ \cline{3-3}  & & Normal & 96.44 & 89.87 & & \\ \hline \multirow{4}{*}{Voting} & \multirow{4}{*}{5} & \multirow{4}{*}{ViTPose} & Fall & 97.70 & 91.57 & \multirow{4}{*}{94.69} \\ \cline{3-3}  & & Normal & 92.01 & 97.83 & & \\ \cline{3-3}  & & & Fall & 95.61 & 89.61 & \multirow{4}{*}{92.72} \\ \cline{3-3}  & & Normal & 90.15 & 95.85 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Action** &
\begin{tabular}{c} **Actual** \\ **Value** \\ \end{tabular} & **Fall** & **Normal** & **Recall (\%)** \\ \hline ViTPose & & & 98 & 4 & 96.08 \\ \hline AlphaPose & 1 & Fall & 101 & 1 & 99.02 \\ \hline ViTPose & & & 88 & 14 & 86.27 \\ \hline AlphaPose & 2 & Fall & 81 & 21 & 79.41 \\ \hline ViTPose & & & 96 & 6 & 94.12 \\ \hline AlphaPose & 3 & Fall & 93 & 9 & 91.18 \\ \hline ViTPose & & & 96 & 6 & 94.12 \\ \hline ViTPose & 4 & Fall & 96 & 6 & 94.12 \\ \hline AlphaPose & 4 & Fall & 92 & 10 & 90.20 \\ \hline ViTPose & & & 99 & 3 & 97.06 \\ \hline AlphaPose & 5 & Fall & 99 & 3 & 97.06 \\ \hline ViTPose & & & 99 & 3 & 97.06 \\ \hline ViTPose & & & 99 & 3 & 97.06 \\ \hline ViTPose & & & 90 & 102 & 100 \\ \hline AlphaPose & 7 & & (Standing) & 0 & 102 & 100 \\ \hline ViTPose & 8 & & (Sitting) & 0 & 102 & 100 \\ \hline ViTPose & & & 4 & 98 & 96.08 \\ \hline AlphaPose & 9 & & 5 & 97 & 95.10 \\ \hline ViTPose & & & 0 & 102 & 100 \\ \hline AlphaPose & 10 & & (Jumping) & 0 & 102 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Number of action recognition results of the probabilistic fusion method with the optimal number of classifiers. In the experiments, the misclassification of falling incidents as normal situations mainly occurred due to failures in person detection. To address this issue, we constructed an additional training dataset with a focus on detecting individuals who were either obscured or in a fallen state, utilizing the HAR-UP dataset, and conducted subsequent experiments. During this process, the training dataset and the test dataset were divided in a 7:3 ratio, and 4375 images were selected from the training videos for training YOLOv7. Figure 10 shows the results obtained using the improved YOLOv7. In Table 8, the performance of the proposed method is compared with that of previous methods using the HAR-UP dataset. The performance of these previous methods is directly cited from other research studies [18; 20; 27; 42; 43].