## 3 Model and Algorithm Design
### CNN-LSTM Model  
In the context of monitoring driver physiological characteristics, Convolutional Neural Networks (CNNs) can be used to analyze image data, such as the driver's facial expressions, or to extract features from sensor data, such as electrocardiograms. On the other hand, Long Short-Term Memory (LSTM) networks excel in handling dynamically changing physiological signals by capturing long-term dependencies, which significantly improves the accuracy of predictions related to the heart rate and respiratory frequency. The primary advantage of LSTM lies in its ability to maintain these dependencies over time, making it particularly well-suited for analyzing time-series physiological data. Through its gating mechanism, LSTM selectively retains or discards information, ensuring stable performance even in the complex and dynamic environments of driving. When monitoring driver physiological characteristics, LSTM is highly effective at dynamically analyzing signals, such as the heart rate and respiratory frequency. It assesses the driver's fatigue and health status by capturing temporal variations in physiological signals. LSTM networks are specifically designed to process time-series data, addressing issues like the vanishing gradient problem, which is crucial for tracking the continuous and dynamic changes in the driver's physiological state. This paper adopts a CNN-LSTM hybrid approach for monitoring driver physiological characteristics. The algorithmic steps are outlined as follows:  
**Step 1:** Data preprocessing is critical for ensuring optimal model performance and includes denoising, normalization, and the handling of missing values. Denoising: Physiological signal data are often contaminated by various sources of noise, including electromagnetic interference and environmental disturbances. To enhance signal quality, a low-pass filter is applied to suppress high-frequency noise. The frequency response of this filter can be represented by the following formula:  
\[H(f)=\frac{1}{1+\left(f/f_{\text{c}}\right)^{2n}} \tag{1}\]  
where \(H(f)\) is the frequency response, \(f\) is the frequency, \(f_{\text{c}}\) is the cutoff frequency, and \(n\) is the order of the filter [2]. By setting an appropriate cutoff frequency, the low-frequency components of the signal can be effectively retained, while unnecessary high-frequency noise is removed, thereby improving the signal-to-noise ratio (SNR). When applied to physiological signal monitoring, a low-pass filtering range of 0.5 Hz to 50 Hz is typically selected to capture variations in the heart rate and respiratory frequency. Normalization: Due to the differences in dimensions and magnitudes of physiological signals, normalization is crucial to ensure that all features contribute equally to model performance. The normalization process can be mathematically represented as follows:  
\[x^{\prime}=\frac{x-\min(X)}{\max(X)-\min(X)} \tag{2}\]  
By transforming the data into the range of [0, 1], issues arising from inconsistent dimensions during model training can be reduced, improving the stability and convergence speed of the training process [7]. This process is particularly important when dealing with multidimensional physiological data, as it allows different features to receive relatively balanced weights during training, thereby enhancing the model's accuracy. Missing Value Imputation: The presence of missing values in physiological signal data can lead to a decline in model performance. To fill in missing values, linear interpolation can be employed, represented by the following formula:  
\[x_{filled}=x_{prev}+\frac{\left(x_{next}-x_{prev}\right)}{\left(t_{next}-t_{ prev}\right)}\left(t-t_{prev}\right) \tag{3}\]  
where \(t\) is the time variable, and \(x_{prev}\) and \(x_{next}\) are the observed values before and after the missing value, respectively. This method effectively estimates the missing data, ensuring data completeness and enhancing the robustness of the model [26].