## 4 Experimental Design and Results
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Driving State** & **Model Type** & **Precision** & **Accuracy** & **Recall** & **F1 Score** & **False Positive Rate** \\ \hline High-speed Driving & CNN-LSTM & 89.66\% & 90.11\% & 90.41\% & 90.23\% & 7.52\% \\ \hline High-speed Driving & SVM & 84.91\% & 85.74\% & 81.08\% & 86.44\% & 8.57\% \\ \hline High-speed Driving & Random Forest & 86.12\% & 86.73\% & 85.28\% & 87.17\% & 8.72\% \\ \hline High-speed Driving & Pure LSTM & 87.85\% & 88.25\% & 87.69\% & 87.92\% & 8.13\% \\ \hline Medium-speed Driving & CNN-LSTM & 86.45\% & 87.61\% & 86.88\% & 88.71\% & 8.28\% \\ \hline Medium-speed Driving & SVM & 84.71\% & 84.35\% & 82.12\% & 83.2\% & 9.21\% \\ \hline Medium-speed Driving & Random Forest & 85.68\% & 86.17\% & 84.78\% & 85.41\% & 8.98\% \\ \hline Medium-speed Driving & Pure LSTM & 85.98\% & 86.93\% & 85.79\% & 86.86\% & 9.47\% \\ \hline Low-speed Driving & CNN-LSTM & 80.27\% & 81.81\% & 80.71\% & 83.95\% & 9.89\% \\ \hline Low-speed Driving & SVM & 74.19\% & 80.41\% & 75.13\% & 80.34\% & 11.41\% \\ \hline Low-speed Driving & Random Forest & 78.15\% & 79.59\% & 79.15\% & 81.21\% & 10.68\% \\ \hline Low-speed Driving & Pure LSTM & 79.82\% & 80.77\% & 79.63\% & 82.07\% & 10.19\% \\ \hline Congested Traffic & CNN-LSTM & 84.55\% & 85.18\% & 85.12\% & 84.88\% & 8.42\% \\ \hline Congested Traffic & SVM & 82.52\% & 82.13\% & 81.21\% & 82.18\% & 10.21\% \\ \hline Congested Traffic & Random Forest & 82.71\% & 83.65\% & 82.29\% & 82.37\% & 10.37\% \\ \hline Congested Traffic & Pure LSTM & 83.13\% & 84.29\% & 84.50\% & 83.41\% & 9.98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of different models in driver-state-monitoring Tasks. it can capture both spatial and temporal features of multi-modal signals. The results underscore the superiority of CNN-LSTM in handling complex multi-modal physiological data for driver-state monitoring, offering enhanced accuracy and robustness compared to traditional machine learning models. This performance validates the CNN-LSTM model as a strong candidate for real-time, accurate, and reliable driver monitoring in various driving conditions. The model utilizing only LSTM performs better than traditional machine learning methods, but it does not match the performance of CNN-LSTM in all driving scenarios. LSTM can effectively process time-series data, especially maintaining a high recall rate in low-speed driving and traffic congestion situations. However, the lack of feature-extraction capability provided by convolutional neural networks results in LSTM's performance falling short compared to CNN-LSTM due to the insufficient extraction of spatial information from physiological signals. Among all models, CNN-LSTM consistently exhibits the lowest false positive rate. In high-speed driving scenarios, the false positive rate of CNN-LSTM is significantly lower than that of SVM and Random Forest models. This indicates that CNN-LSTM can better differentiate between normal and abnormal states of drivers, thereby reducing the occurrence of false alarms. Through comparative experiments, it can be concluded that the proposed CNN-LSTM model demonstrates excellent performance in handling multi-modal driver-monitoring tasks, achieving high monitoring accuracy and low false positive rates across various scenarios, including high-speed, fast, low-speed driving, and traffic congestion. In contrast, traditional machine learning methods (such as SVM and Random Forest) perform relatively poorly when faced with complex, multi-modal signals. Although the LSTM model possesses certain advantages in time-series processing, its lack of the effective extraction of spatial features from multi-modal signals results in inferior performance compared to CNN-LSTM.