## 4 Experimental Design and Results
### Experimental Results and Analysis
This performance suggests that the CNN-LSTM model can handle complex, real-time driving scenarios and effectively monitor the physiological state of the driver even in situations where stress and tension are heightened. Its ability to maintain high accuracy in such dynamic contexts showcases its potential for use in emergency response systems within vehicles, further enhancing driver safety. The convergence analysis of the loss function, as shown in Figure 4, further supports the model's effectiveness. The CNN-LSTM model exhibits rapid convergence in both the training and validation datasets, with the validation loss stabilizing after around 104 epochs. This fast convergence indicates that the model is well-optimized, efficiently learning from the data, and able to generalize well to new, unseen data. The stabilization of the validation loss also suggests that the model is robust, without overfitting, and is likely to perform consistently across different datasets and real-world applications. The training process of the CNN-LSTM model shows promising results in terms of model fitting and generalization capability. As training progresses, the training loss gradually decreases and stabilizes, indicating that the model is improving in its ability to fit the training data. This reflects the model's ability to learn and extract useful features that progressively move toward the optimal solution. The stabilization of the validation loss after the 104 th epoch further demonstrates that the model is not overfitting the training data, maintaining strong generalization performance based on unseen data. This outcome can likely be attributed to the incorporation of regularization techniques and the use of dropout layers, which help prevent overfitting by randomly dropping neurons during training, ensuring that the model does not become too reliant on any one feature. To rigorously evaluate the performance of the proposed CNN-LSTM multimodal data-fusion model in monitoring drivers' physiological states, this study includes a set of comparative experiments with several other existing driver monitoring models. The comparison includes traditional machine learning-based approaches, such as Support \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\backslash\) & **Predicted Normal** & **Predicted Fatigue** & **Predicted Emergency** \\ \hline Actual Normal & 834 & 38 & 28 \\ \hline Actual Fatigue & 37 & 817 & 46 \\ \hline Actual Emergency & 29 & 45 & 826 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix. Vector Machines (SVM) and Random Forest (RF), as well as a standalone LSTM model that uses only sequential data without the convolutional feature-extraction component. These comparisons will provide valuable insights into the strengths and weaknesses of the CNN-LSTM model, as well as its relative performance against conventional methods. The comparative experiments are conducted using the same multimodal dataset, which contains the physiological signals of drivers, including the heart rate, breathing frequency, skin conductance, and body pressure distribution. This dataset is standardized to ensure that the numerical ranges of all modalities are similar, preventing any single modality from disproportionately influencing the model's predictions. Additionally, the dataset is split into a training set (70%) and a testing set (30%), with cross-validation employed to ensure the robustness of the model and avoid any potential bias due to overfitting to specific data partitions. By evaluating the CNN-LSTM model against these traditional machine learning methods and a pure LSTM model, this study aims to comprehensively assess the advantages of incorporating convolutional layers for spatial feature extraction and temporal sequence modeling in multimodal driver-physiological-state monitoring. The results of these experiments will demonstrate whether the CNN-LSTM approach outperforms the other methods in terms of precision, recall, accuracy, and overall effectiveness in the real-time monitoring of driver health.