## 5 Conclusions and Outlook
To ensure the external validity of the system, future research should focus on simulating more complex driving environments that account for these real-world variables. Data collection in real-world conditions, such as under varied weather, during high-speed driving, and in dense traffic, would provide a more accurate representation of the challenges drivers face and help assess the model's robustness in these scenarios. Additionally, incorporating environmental sensors that measure weather conditions or road quality could enhance the model's performance by allowing it to adjust predictions based on external factors. For example, if the system detects that the temperature is high or the road is slippery, it could adjust its monitoring algorithms accordingly, improving the accuracy of the physiological state predictions. To address the challenges of individual variability and real-world applicability, future research could pursue several directions:  
Personalized Calibration and Adaptation: Implementing a personalized calibration phase that learns individual physiological profiles could help the model adapt to each driver's unique characteristics. This could be achieved through online learning, allowing the system to adjust its predictions based on continuous data collection and refinement of the model's parameters. Simulating Complex Driving Environments: To ensure the system's robustness in real-world conditions, future studies should simulate driving scenarios that reflect the variability of weather, traffic congestion, and long-distance driving. Collecting data from a wider range of driving environments will help validate the model's ability to maintain high accuracy in diverse conditions. Integration of Additional Physiological Signals: Incorporating more comprehensive physiological data, such as blood oxygen saturation, EMG, or ECG, could provide more detailed insights into a driver's health and stress levels. This could improve the model's sensitivity to fatigue and stress, especially in cases where traditional signals, like skin conductance, may not be sufficient. Real-Time Performance Optimization: Given the importance of real-time monitoring, future research should focus on optimizing the computational efficiency of the model. Techniques like model pruning or edge computing could be explored to reduce the processing time and enable faster decision-making, ensuring that the system can respond promptly in dynamic driving environments. In conclusion, the CNN-LSTM-based multimodal physiological monitoring system demonstrates strong potential for driver health assessments and fatigue detection, offering a promising solution for intelligent transportation systems. While the model performs well in controlled experiments, there is significant room for improvement, particularly in terms of personalization and real-world applicability. By addressing individual variability and simulating more complex driving scenarios, future research can enhance the system's robustness and ensure its widespread use in real-world applications. This system has the potential to significantly contribute to road safety, driver well-being, and the development of more effective fatigue detection and safety assurance systems in autonomous and intelligent transportation contexts. **Author Contributions:** Conceptualization, S.Z. and J.Y. ; methodology, J.X. ; software, Q.D. and X.L. ; validation, Q.D., J.X. and S.Z. ; formal analysis, J.X. and L.W. ; investigation, S.Z. ; resources, N.Z; data curation, N.Z. ; writing--original draft preparation, S.Z. ; writing--review and editing, Q.D. and L.W. ; visualization, J.X. ; supervision, S.Z. ; project administration, J.Y. ; funding acquisition, N.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This Research was Financially Supported by Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection (No. 2023B1212010011); Foshan Self-Funded Science and Technology Innovation Projects (No.