## 4 Experimental Design and Results
### Experimental Results and Analysis
The system's overall performance in high-speed driving was also the best, with an accuracy of 90.11% and a recall of 90.41%, indicating strong sensitivity to changes in the driver's physiological state. These results can be attributed to the significant fluctuations observed in key physiological signals, such as the heart rate and respiratory frequency, during high-speed driving. These fluctuations allow the multimodal fusion system to effectively detect states of fatigue or tension, significantly reducing the false positive rate to 7.52%. In medium-speed driving conditions, the system's precision was slightly lower at 86.45%, but it still demonstrated high accuracy, indicating that the system can maintain good precision when assessing the driver's fatigue or tension states. The accuracy was 87.61%, with a recall of 86.88%. These results suggest that in medium-speed driving, the fluctuations in the driver's physiological state are less pronounced, leading to slightly reduced sensitivity in detecting certain states. However, the overall performance remains strong, with the false positive rate at 8.28%, which is higher than in the high-speed scenario, but still within an acceptable range. In low-speed driving conditions, the system's precision was 80.27%, and the recall was 80.71%, indicating a balanced performance in terms of both precision and sensitivity. However, due to smaller physiological fluctuations--particularly in the heart rate and respiratory frequency--the system faces more challenges in detecting fatigue or tension states in low-speed driving. This is reflected by the relatively higher false positive rate of 9.89%. Despite this, the F1 score of 83.95% shows that the system performs well in terms of balancing both precision and recall under low-speed driving conditions. The higher false positive rate in low-speed driving may be attributed to the lack of significant fluctuations in physiological signals, causing the system to misinterpret minor variations in the data. These findings demonstrate that the multimodal data-fusion system is robust across various driving conditions, with the highest accuracy and lowest false positive rate in high-speed driving, where physiological signals exhibit larger fluctuations. However, in low-speed driving, the system's performance is slightly compromised due to less-pronounced physiological changes, leading to a higher false positive rate. In congested traffic conditions, the system exhibited a precision of 84.55%, demonstrating good accuracy, especially in identifying tense states. Given the frequent stress responses of drivers in congested environments, the system could effectively capture these changes. The recall was 85.12%, indicating the system's sensitivity in recognizing tension states in response to congestion. The F1 score was 84.88%, reflecting a good balance between precision and recall, with a false positive rate of 8.42%, slightly higher than in high-speed driving, but still capable of effectively handling the complexities of congested scenarios. To further illustrate the system's performance balance under different driving conditions and to analyze the differences in accuracy and sensitivity in specific scenarios, Figures 2 and 3 present the trends in the precision, recall, F1 score, false positive rate, and accuracy as indicators of driving states. The experimental results underscore the effectiveness of the complete multimodal data-fusion system in accurately monitoring driver physiological states under varying driving conditions, with particularly high performance under high-speed and congested traffic scenarios. By integrating diverse physiological signals--the heart rate, respiratory frequency, skin conductance, and body pressure distribution--the system achieves enhanced monitoring accuracy and robustness. This multimodal fusion technology plays a crucial role in improving precision and recall, as it allows for the more accurate identification of abnormal states by effectively filtering noise and reducing false positives across different scenarios.