## 4 Experimental Design and Results
### Ablation Experiment
The role of skin conductance signals is mainly evident in situations with significant emotional fluctuations, such as high-speed driving and traffic congestion, where they can reflect the driver's stress responses. However, in driving scenarios with lower stress, their contribution is relatively limited. Body pressure distribution signals are used to monitor the driver's sitting posture and posture changes, primarily reflecting the driver's adjustments in posture under fatigue conditions. During high-speed driving, drivers usually maintain a relatively fixed posture, leading to a small contribution from body pressure distribution signals. After removing this signal, the system's accuracy decreased by only 4.43%, indicating a limited role in this scenario. In rapid driving, body pressure distribution signals can assist in detecting the driver's posture adjustments; after removing this signal, the accuracy decreased by 4.06%, indicating a certain value in fatigue monitoring. Under low-speed driving conditions, the contribution of body pressure distribution signals is more noticeable, especially after prolonged driving, as drivers may adjust their posture to alleviate fatigue. After removing this signal, the system's accuracy decreased by 5.02%. In congested conditions, frequent braking and acceleration may lead to posture adjustments by the driver. After removing body pressure distribution signals, the system's accuracy decreased by 4.81%, indicating that this signal can reflect changes in the driver's physical state in this scenario. Body pressure distribution signals play a prominent role in monitoring fatigue driving, particularly in low-speed driving and traffic congestion scenarios, effectively capturing the driver's fatigue status. Overall, heart rate signals are the most important signals across all driving conditions, with a significant decline in system performance after their removal. Respiratory rate signals also contribute greatly during rapid driving and traffic congestion, particularly in recognizing tense states. The contributions of skin conductance signals and body pressure distribution signals are relatively smaller in different driving scenarios, but they still provide certain auxiliary information in specific contexts (such as congestion and low-speed driving). Figure 5 shows the changes in system accuracy across different driving states after removing various modality signals. The four curves represent the accuracy changes of the system after removing the heart rate, respiratory rate, skin conductance, and body pressure distribution signals. These curves reveal the importance of each modality signal in different driving scenarios. The trend of the curves displays the degree of performance decline of the system in various scenarios after the removal of a specific modality. Figure 6a illustrates the contributions of different modality signals (heart rate, respiratory rate, skin conductance, body pressure distribution) to system performance under various driving states. This can be represented by the weight variation curves of each modality signal, showcasing the importance of each signal in different driving conditions. The curve reveals how the multimodal fusion system adapts by adjusting signal weights in various driving scenarios, highlighting the contribution of each signal. Figure 6b illustrates the variation in system accuracy across four different driving states: high-speed driving, rapid driving, low-speed driving, and traffic congestion. Each curve represents the contribution of a specific sensor type to the overall system accuracy in detecting fatigue and stress. The fluctuations in each curve reflect how the importance of each sensor varies across different driving scenarios. For instance, the heart rate and respiratory rate are key contributors in high-speed and rapid driving conditions, whereas skin conductance and body pressure distribution become more influential in low-speed driving and traffic congestion.