## 4 Experimental Design and Results
### Experimental Results and Analysis  
To validate the effectiveness of the proposed CNN-LSTM model in monitoring driver physiological characteristics, a comprehensive series of comparative experiments was conducted. These experiments aimed to assess the model's performance under various conditions, including multimodal data fusion, and to evaluate its applicability and robust ness in real-world driving scenarios. The experimental content is divided into several key components, as outlined below:  
1. Precision Precision measures the proportion of correctly predicted positive samples out of all the predicted positive samples. It is calculated as Precision = True Positives/(True Positives + False Positives). This metric reflects the system's ability to accurately determine whether the driver is in a specific state (e.g., fatigue or stress). A higher precision value indicates fewer false alarms. 2. Accuracy Accuracy is the proportion of correctly classified driver states to the total number of samples. It is defined as Accuracy = (True Positives + True Negatives)/Total Samples. Accuracy represents the overall monitoring capability of the system, considering both the correct identification of driver states and the correct classification of non-critical states. 3. Recall Recall measures the proportion of correctly identified instances of a specific physiological state (such as fatigue or stress) out of all actual instances of that state. It is calculated as Recall = True Positives/(True Positives + False Negatives). Recall reflects the system's sensitivity and its ability to correctly identify specific states. A higher recall value indicates that the system is effective at detecting physiological states of interest, such as fatigue or stress, even when they occur infrequently. 4. F1 Score The F1 score is the harmonic mean of precision and recall, providing a balanced measure of the system's performance. It is calculated as F1 Score = 2 \(\times\) [(Precision \(\times\) Recall)/(Precision + Recall)]. The F1 score helps balance the trade-off between precision and recall, providing a more comprehensive view of the system's performance, particularly when dealing with imbalanced datasets or varying detection rates across different physiological states. 5. False Positive Rate The false positive rate (FPR) measures the proportion of incorrectly identified positive states (i.e., when the system incorrectly classifies a non-target state as a target state). It is calculated as FPR = False Positives/(False Positives + True Negatives). A lower false positive rate indicates that the system is more robust and less likely to make erroneous predictions, which is critical for maintaining the driver's trust in the system and avoiding unnecessary interventions. The use of these performance metrics allows for a thorough evaluation of the multimodal data fusion system's ability to monitor the physiological states of the driver in real-world driving conditions. By assessing the system's precision, accuracy, recall, F1 score, false positive rate, and real-time performance, the study provides a comprehensive analysis of the model's capabilities and robustness, demonstrating its effectiveness in ensuring driver safety and well-being. Through experimental comparisons of different input signal combinations and model architectures, the following results shown in Table 1 were obtained under comprehensive working conditions:  
From experimental results presented in the table, it can be observed that the CNN-LSTM model achieves the best performance with tri-modal signal input, demonstrating an accuracy of 86.38% and an F1 score of 85.07%. This performance notably surpasses that of both single-modal and dual-modal input models, highlighting the advantages of integrating multiple physiological signals for driver state monitoring. Furthermore, the CNN-LSTM model outperforms the traditional MLP model, showcasing the effectiveness of deep learning architectures in capturing the complex patterns in multimodal physiological data.