## 4 Experimental Design and Results
### Experimental Results and Analysis
One of the key advantages of the system lies in its adaptive weighted fusion algorithm, which dynamically adjusts the weights of each physiological signal based on real-time data quality. This adaptive mechanism optimizes the contribution of each signal, tailoring the system's response to the specific driving conditions at hand. For instance, during high-speed driving, the system places greater emphasis on heart rate and respiratory frequency signals, as these physiological indicators tend to show more pronounced fluctuations. In contrast, in congested traffic scenarios, skin conductance signals become more critical for detecting the driver's tension and stress levels. This dynamic adjustment helps the system effectively respond to the varying nature of physiological signals in different driving environments. The increase in precision observed in the experiments reflects the system's ability to mitigate false positives, which is especially important in complex and dynamic driving conditions. By leveraging multisource signals and adjusting their contributions based on the driving context, the system ensures a high level of accuracy, maintaining reliability and stability even under challenging scenarios, such as high-speed driving and heavy traffic. The results confirm that multimodal data fusion not only improves the detection of physiological states but also enhances the robustness of the monitoring system, making it well-suited for real-world driver health assessment applications. Despite the system's high precision and accuracy across various driving conditions, there remains a certain level of the false positive rate, especially in low-speed driving and congested traffic states, where the false positive rate is relatively high. This is primarily due to the frequent operations of drivers in these scenarios and the smaller fluctuations in physiological signals, which may lead the system to misinterpret minor signal changes as fatigue or tension. To further reduce the false positive rate, signal-processing algorithms can be optimized to enhance tolerance for short-term signal fluctuations, or time-series analysis techniques from deep learning can be employed to improve the system's response capabilities in complex scenarios. Figure 3: Curves showing the variation in false positive rates and accuracy with driving states. An analysis of the model's performance under different driving states (normal, fatigue, emergency situations) was conducted, with 300 samples selected for prediction from each state. The specific confusion matrix from the experiments is shown in Table 3.  
The experimental results further highlight the robustness and effectiveness of the CNN-LSTM model in monitoring driver physiological characteristics across various driving states. Under normal driving conditions, the model achieves a high correct identification rate of 92.67%, indicating its strong capability in accurately assessing the physiological state of a driver who is not fatigued or under stress. This suggests that the system is highly effective in monitoring the baseline health condition of a driver, ensuring safety and comfort during routine driving. When the driver is in a fatigued state, the model's correct identification rate drops slightly to 90.78%, but it still performs well, highlighting the model's sensitivity to changes in physiological indicators associated with fatigue. Fatigued drivers tend to show notable physiological alterations--such as fluctuations in the heart rate and respiratory frequency--making it easier for the model to detect and identify these states with good accuracy. This reinforces the model's potential to serve as a safety mechanism to warn drivers and prevent accidents caused by fatigue. In emergency situations, where driving conditions change rapidly (e.g., sudden braking or steering), the model's accuracy remains high at 91.78%, demonstrating its adaptability and robustness in dynamic and high-pressure environments.