## 5 Conclusions and Outlook  
This study proposes a hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for the efficient and accurate monitoring of drivers' physiological states. By integrating multiple physiological signals, such as the heart rate, respiratory rate, and skin conductance, the system demonstrates significant potential for driver health assessment, particularly in detecting fatigue and stress. The model achieves a maximum accuracy of 86.38%, showcasing its ability to handle complex physiological data and perform well across various driving scenarios. One of the main strengths of the proposed CNN-LSTM model is the use of multimodal signals, which significantly enhances both the accuracy and reliability of the monitoring system. The fusion of spatial feature extraction by CNNs with temporal modeling by LSTMs allows the model to capture both the short-term and long-term dependencies in physiological signals, improving real-time monitoring in dynamic driving environments. Ablation experiments further demonstrate the importance of each physiological signal, particularly highlighting the significant role of skin conductance in assessing drivers' psychological states. This provides valuable insights for the future optimization of real-time monitoring systems. However, while the model performs well in controlled experimental settings, there are several areas that require attention for improving its real-world applicability. A key challenge for physiological monitoring systems is individual variability. While physiological signals, like the heart rate and skin conductance, are generally reliable indicators of fatigue and stress, their effectiveness can vary significantly between individuals. Factors, such as baseline physiological states, autonomic nervous system reactivity, age, gender, and health conditions, can all influence how a person's body responds to stress or fatigue, leading to variability in signal patterns. For example, some drivers may exhibit minimal changes in skin conductance, even under high stress, while others may show more pronounced reactions. This individual variability could affect the model's accuracy, especially if it does not account for the unique physiological profiles of different drivers. To address this issue, future work could focus on personalizing the model to accommodate these individual differences. One possible solution is to introduce a personalized calibration phase, where the system learns the baseline physiological signals of each driver over time. This approach would allow the system to adjust its predictions based on the unique physiological characteristics of each driver, improving the model's adaptability and reducing potential inaccuracies due to individual variation. In addition to this, incorporating more physiological signals, such as blood pressure, electromyography (EMG), or electrocardiogram (ECG) data, could provide a more comprehensive view of a driver's physiological state. These additional signals can help the  
Figure 7: Variation curve of driving state and reaction time. model to better identify fatigue and stress in individuals whose physiological responses may not be well-captured by skin conductance alone, further enhancing the accuracy and robustness of the system. While the experimental setup used in this study provides valuable insights, it may not fully simulate the complexities of real-world driving conditions. Real-life driving is influenced by a variety of factors, including adverse weather conditions, traffic congestion, and long-distance driving, all of which can impact a driver's physiological responses. For example, extreme temperatures, humidity, or precipitation can influence heart rate and respiratory patterns, while long-duration driving can introduce fatigue that might not be adequately captured in short, controlled trials.