# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 5 Experimental Evaluation
### State-of-the-Art Comparison for the UR-FALL Multimodal Dataset  
We conduct the experimental evaluation on the UR Fall multimodal dataset and compare our results with state-of-the-art methods, as shown in Table 7, including data modalities, methods, and performance metrics. [PERSON] et al. [50] propose a low-cost, 24/7 embedded fall detection system that combines accelerometer data and depth maps. They use an SVM classifier to reduce false positives and report an accuracy of 94.28%. [PERSON] et al. [71] introduce a fall detection method based on video and 2D body pose estimation. By tracking body movements over time and applying an SVM model, they achieve an accuracy of 96.55%. [PERSON] et al. [72] propose a deep learning-based method using an hourglass-shaped network that also reconstructs video frames to reduce information loss. Their approach reaches an accuracy of 90.50%. [PERSON] et al. [73] present a method designed for complex visual backgrounds. They use Mask R-CNN to extract moving objects and an attention-guided Bi-LSTM to detect falls, reporting an accuracy of 96.70%. [PERSON] et al. [41] combine an improved YOLOv4 with GhostNet and BiFPN for fast human detection. AlphaPose is used for pose estimation, and ST-GCN is applied for action recognition, achieving a high accuracy of 97.28% on the UR Fall dataset. [PERSON] et al. [74] propose a visual fall detection method based on Dual-Channel Feature Integration, which separates fall events into \"falling\" and \"fallen\" states. They use YOLO and OpenPose to extract dynamic and static features, classified using MLP and Random Forest. Their combined approach achieves an accuracy of 97.33%. In comparison to these studies, our proposed model achieves a significantly higher accuracy of 99.32% using multimodal data. This demonstrates the strong performance and effectiveness of our system in leveraging multimodality for fall detection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fold** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** & **F1-Score [\%]** \\ \hline
1 & 100 & 100 & 100 & 100 \\ \hline
2 & 99.31 & 99.53 & 98.70 & 99.11 \\ \hline
3 & 100 & 100 & 100 & 100 \\ \hline
4 & 99.68 & 99.80 & 99.23 & 99.51 \\ \hline
5 & 96.69 & 96.68 & 96.71 & 96.68 \\ \hline
6 & 99.68 & 99.17 & 99.80 & 99.48 \\ \hline
7 & 100 & 100 & 100 & 100 \\ \hline
8 & 99.42 & 98.65 & 99.63 & 99.13 \\ \hline
9 & 100 & 100 & 100 & 100 \\ \hline
10 & 98.38 & 98.47 & 97.83 & 98.14 \\ \hline Average & 99.32 & 99.23 & 99.19 & 99.21 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cross-validation fold-wise precision, recall, and F1-score for UR-Fall multimodal dataset.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Author** & **Data Modality** & **Method Name** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** \\ \hline [PERSON] [50] & Depth & SVM & 94.28 & - & - \\ \hline [PERSON] [71] & Skeleton & SVM & 96.55 & - & - \\ \hline [PERSON] [72] & - & HCAE & 90.50 & - & - \\ \hline [PERSON] et al. [73] & RGB & Bi-LsTM & 96.70 & - & - \\ \hline [PERSON] [41] & Skeleton & & 97.28 & 97.15 & 97.43 \\ \hline \hline \end{tabular}
\end{table}
Table 7: State-of-the-art comparison of the proposed model on the UR Fall multimodal dataset.