## 3 Model and Algorithm Design
### CNN-LSTM Model
The fully connected layer classifies the last hidden state output from the LSTM using the Softmax activation function:  
\[x^{\prime}=\frac{x-\min(X)}{\max(X)-\min(X)} \tag{2}\]5  
This layer is responsible for generating the final prediction results, such as the driver's health status or fatigue level, providing important real-time feedback to ensure driving safety. Through this process, the model can convert the input physiological signals into specific evaluations of the driver's state [7]. Step 6: Model Training. The Adam optimization algorithm is employed to train the model and update its parameters via backpropagation. The Adam optimizer combines the benefits of adaptive learning rates with momentum, making it efficient for training deep networks. The update rules for the Adam optimizer are as follows:  
\[ \begin{array}{l}m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})\
abla L\\ v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})(\
abla L)^{2}\\ \theta=\theta-\frac{\gamma p_{t}}{\sqrt{m_{t}+\epsilon}}\end{array}  \tag{16}\]  
where \(\beta_{1}\) and \(\beta_{2}\) are the momentum decay rates, \(\eta\) is the learning rate, and \(\epsilon\) is a small constant to prevent division by zero. With these update rules, the model can adaptively adjust the learning rate, improving training efficiency. The Adam algorithm combines the advantages of Momentum and RMSProp, allowing for an adaptive learning rate adjustment, enhancing convergence efficiency, and accelerating the training process. A dropout layer is introduced to prevent overfitting, expressed as follows:  
\[h_{dropout}=h\cdot r \tag{17}\]  
where \(r\) is a random variable used to randomly drop some neurons during training, thereby increasing the model's generalization ability. By appropriately setting the dropout rate, the model's performance based on unseen data can be effectively improved. The CNN-LSTM combination method is highly effective for monitoring driver characteristics, as it extracts spatial features from physiological signals through convolutional layers and captures temporal dependencies via LSTM. This hybrid approach allows the model to integrate multimodal data, improving both the accuracy and real-time assessment of driver health, thus enhancing driving safety. However, the performance of a single model may be compromised by challenges, such as data imbalances and noise interference. To address these issues, ensemble learning techniques are employed to boost the model's robustness and prediction accuracy. The ensemble methods adopted in this study include voting mechanism (deciding the final prediction label through the voting results of multiple models to mitigate the bias of a single model, improving the overall prediction stability) [29], Bagging and Boosting (Bagging (e.g., random forests) trains multiple models by re-sampling the training data and averaging their results; Boosting progressively enhances the performance of weak models through weighted methods. Both methods can significantly improve predictive performance), and hyperparameter optimization (optimizing model hyperparameters through a grid search or random search; in LSTM, the learning rate, batch size, and number of layers are important hyperparameters of which impacts can be assessed through cross-validation. The goal of optimization is to find the best combination of hyperparameters that minimizes the loss based on the validation set).