## 4 Experimental Design and Results
### Experimental Results and Analysis
The results underscore the importance of tri-modal signal fusion in enhancing the model's predictive accuracy for monitoring the driver's physiological states. By combining heart rate, respiratory rate, and electrodermal activity (EDA) signals, the CNN-LSTM model leverages complementary information from different modalities, improving its ability to detect subtle changes in the driver's health condition. In particular, the contribution of skin conductance (EDA) signals is particularly notable in the detection of stress. The EDA signals, which reflect the driver's emotional arousal and stress levels, play a significant role in accurately identifying stressful conditions in complex driving environments. This finding suggests that incorporating EDA data provides valuable insights into the driver's emotional state, which is crucial for real-time health and fatigue monitoring. Tri-modal Fusion: Combining heart rate, respiratory rate, and EDA signals yields superior predictive performance compared to single-modal and dual-modal models. The integration of these modalities provides a more comprehensive understanding of the driver's physiological state. Enhanced Accuracy: The CNN-LSTM model, with tri-modal input, achieves an accuracy of 86.38%, which is significantly higher than that of single-modal or dual-modal models, demonstrating the effectiveness of multimodal fusion in physiological state monitoring. Role of EDA Signals: The electrodermal activity (EDA) signals, which measure skin conductance, have a particularly strong contribution to stress perception. In dynamic and complex driving environments, the EDA data help the model distinguish stressful situations, making it a key factor in improving the accuracy of stress detection. This experiment highlights the critical role of multimodal data fusion in improving driver health monitoring systems, particularly in complex driving scenarios where physiological states, such as stress and fatigue, must be detected with high accuracy. To refine the analysis dimensions, experiments were conducted in a tri-modal manner under four driving conditions: high-speed driving (\(\geq\)80 km/h), medium-speed driving (40-80 km/h), low-speed driving (20-40 km/h), and congested traffic (\(\leq\)20 km/h), followed by a detailed analysis. The performance of the multimodal data-fusion system under four driving states is shown in Table 2.  
In high-speed driving scenarios, the system demonstrated impressive performance, achieving a precision of 89.66%. This high precision reflects the system's ability to accurately identify whether the driver is in a state of fatigue or tension, with minimal false positives. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Type** & **Precision** & **Accuracy** & **Recall** & **F1 Score** &
\begin{tabular}{c} **False** \\ **Positive Rate** \\ \end{tabular} \\ \hline Single-modal (HR) & 76.32\% & 78.23\% & 78.86\% & 76.83\% & 10.18\% \\ \hline Dual-modal (HR + BR) & 80.08\% & 82.15\% & 83.21\% & 80.17\% & 8.15\% \\ \hline Tri-modal (HR + BR + EDA) & 84.18\% & 86.38\% & 85.97\% & 85.07\% & 7.04\% \\ \hline Baseline Model (MLP) & 78.24\% & 80.87\% & 80.04\% & 78.18\% & 9.02\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results under different modalities. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Driving State** & **Precision** & **Accuracy** & **Recall** & **F1 Score** &
\begin{tabular}{c} **False** \\ **Positive Rate** \\ \end{tabular} \\ \hline High-speed Driving & 89.66\% & 90.11\% & 90.41\% & 90.23\% & 7.52\% \\ \hline Medium-speed Driving & 86.45\% & 87.61\% & 86.88\% & 88.71\% & 8.28\% \\ \hline Low-speed Driving & 80.27\% & 81.81\% & 80.71\% & 83.95\% & 9.89\% \\ \hline Congested Traffic & 84.55\% & 85.18\% & 85.12\% & 84.88\% & 8.42\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the multimodal data-fusion system under four driving states.