## 4 Experimental Design and Results
### Experimental Results and Analysis
To ensure fairness in the experiments, all models are fine-tuned, selecting the optimal hyperparameters for training. The CNN-LSTM model employs a two-layer convolutional neural network for feature extraction, followed by LSTM layers for processing time-series data. The SVM model utilizes an RBF kernel, while the Random Forest model is set to 100 trees. The LSTM model is similar to the CNN-LSTM model, except that it omits the convolutional layers. The evaluation metrics for all models include the precision, accuracy, recall, F1 score, and false positive rate. Each metric is calculated based on the model's performance under different driving conditions (high-speed driving, medium-speed driving, low-speed driving, and congested traffic). The experimental results are presented in Table 4, showcasing the performance of different models in the driver-state-monitoring task. The results clearly demonstrate that the CNN-LSTM combined model outperforms other methods in both the accuracy and F1 score, highlighting its strong effectiveness in monitoring driver physiological characteristics. Specifically, the model excels in high-speed and fast driving scenarios, where it achieves higher monitoring accuracy in handling complex driving environments. Figure 4: Convergence of the loss function. By integrating the feature-extraction capabilities of Convolutional Neural Networks (CNNs) with the time-series modeling strengths of Long Short-Term Memory (LSTM), the CNN-LSTM model can effectively capture both the spatial and temporal features of multi-modal signals, enabling the more precise and reliable monitoring of driver states. This combination enhances the model's ability to recognize physiological changes, such as fatigue, stress, and other critical states, with greater accuracy. SVM and Random Forest models show weaker performance, especially when handling non-linear and complex multi-modal data. These traditional machine learning models struggle to process and fuse data from multiple sensors effectively, which limits their capacity to accurately monitor driver states in real-world, dynamic environments. In particular, SVM exhibits a low recall rate in high-speed driving and traffic congestion scenarios, which hinders its ability to detect fatigue or stress states accurately. While Random Forest can handle some of the complexities, it still falls short compared to deep learning-based models like CNN-LSTM, which are better equipped to manage the temporal dependencies and multi-modal fusion necessary for driver monitoring. SVM and Random Forest also lack the ability to model the intricate temporal dynamics present in physiological signals, making them less effective in real-time monitoring. The CNN-LSTM model addresses these challenges by combining CNN's ability to extract spatial features from multi-modal physiological signals with LSTM's strength in modeling the temporal dynamics of those signals. This dual approach allows the CNN-LSTM model to effectively capture both the spatial patterns (such as trends in heart rate, skin conductance, etc.) and temporal patterns (such as variations over time due to driving speed or stress). As a result, the CNN-LSTM model demonstrates superior performance across all driving scenarios, particularly in high-speed and complex traffic conditions where rapid changes in driver state are common. It achieves higher accuracy, better recall, and lower false positives than the traditional methods, making it more reliable for real-time driver-state monitoring. The CNN-LSTM combined model outperforms traditional methods, such as SVM and Random Forest, particularly in high-speed driving and traffic congestion, where