# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 5 Experimental Evaluation
### State-of-the-Art Comparison for UP-Fall Dataset  
Table 5 compares our proposed model's performance with several state-of-the-art fall detection models. The results in this table are taken directly from the corresponding papers, where the performance of each model on similar datasets is reported. We aim to highlight how our model compares to these existing systems using identical or comparable multimodal datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fold** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** & **F1-Score [\%]** \\ \hline k = 1 & 99.35 & 98.65 & 98.29 & 98.45 \\ \hline
2 & 99.44 & 98.25 & 98.15 & 98.14 \\ \hline
3 & 99.45 & 97.48 & 98.28 & 97.84 \\ \hline
4 & 99.58 & 98.90 & 98.67 & 98.76 \\ \hline
5 & 98.2 & 94.62 & 95.16 & 94.86 \\ \hline
6 & 98.96 & 95.97 & 97.20 & 96.31 \\ \hline
7 & 98.75 & 96.46 & 96.25 & 96.15 \\ \hline
8 & 98.42 & 95.79 & 95.44 & 95.43 \\ \hline
9 & 99.38 & 97.69 & 97.27 & 97.38 \\ \hline
10 & 99.33 & 96.83 & 97.08 & 96.67 \\ \hline Average & 99.09 & 97.064 & 97.18 & 96.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cross-validation fold-wise precision, recall, and F1-score for UP-Fall multimodal dataset. \begin{table}
\begin{tabular}{l l l l l l l} \hline \multirow{2}{*}{**Ablation**} & \multirow{2}{*}{\begin{tabular}{c} **Stream-1** \\ **Num of GSTCN** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Stream-2** \\ **BiLSTM-CNN** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Result with UR-FALL (10 Fold Mean)** \\ **Accuracy** \\ \end{tabular} } \\  & & & & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & 3 & 1 & 99.14 & 99.06 & 99.041 & 99.04 \\ \hline
2 & 4 & 1 & 99.15 & 99.19 & 98.81 & 98.99 \\ \hline
3 & 5 & 1 & 99.24 & 99.12 & 99.19 & 99.15 \\ \hline
4 & 6 & 1 & 99.16 & 99.20 & 98.48 & 98.82 \\ \hline
5 & 9 & 1 & 99.32 & 99.23 & 99.19 & 99.21 \\ \hline \end{tabular}
\end{table} 
Table 3: Ablation study of the proposed model for UR Fall multi-modal dataset. [PERSON] et al. (2017) employed a multimodal approach that combined skeleton data with multi-sensor time-series signals. Their system achieved 91.51% accuracy, 90.00% precision, and 91.00% recall. The results were derived from preprocessing techniques such as noise reduction, silhouette extraction, and keypoint identification. In contrast, our proposed model outperforms [PERSON] et al. 's system across all metrics, achieving 99.09% accuracy, 97.06% precision, 97.18% recall, and 96.99% F1 score. Our model integrates multimodal features using a dynamic fusion mechanism, enabling it to capture complex temporal and spatial dependencies more effectively than [PERSON] et al. 's basic fusion approach. Additionally, our advanced noise reduction and augmentation techniques contribute to more accurate feature extraction, even in noisy environments. [PERSON] et al. (2016) proposed a fall detection system using a multi-sensor approach, combining Inertial Measurement Unit (IMU) and Electroencephalogram (EEG) data, and applied a Support Vector Machine (SVM) classifier. Their system achieved 90.77% accuracy, though precision and recall metrics were not reported. This method emphasizes the combination of sensor signals, with EEG offering a unique but untested modality for fall detection. [PERSON] et al. (2014) utilized a multi-sensor system, the MS-DLD, integrating data from multiple inertial sensors. The system achieved 88.75% accuracy, but precision and recall were not reported. Although their approach highlights the importance of multi-sensor data, it shows lower accuracy compared to others in the comparison. [PERSON] et al. (2016) used a Naive Bayes classifier to process multi-sensor data for fall detection. Their system achieved 88.61% accuracy, with precision and recall metrics not disclosed. The simplicity of the Naive Bayes classifier makes it an interesting choice, though it may not capture complex patterns as effectively as more advanced methods. The primary difference lies in how we handle multimodal data. While [PERSON] et al.