## 2 Algorithm and Methodology
### Sliding Window Model

Table 6: Accuracy comparison of different fatigue detection methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Step** &
\begin{tabular}{c} **Weight Combinations** \\ **(BE, YF, NF)** \\ \end{tabular} & **Adjustment Strategy** & **Detection Accuracy** \\ \hline
1 & \(W_{1}\) = 0.30, \(W_{2}\) = 0.30, \(W_{3}\) = 0.40 & Initial setup (biased towards NF) & 88.9 \\
2 & \(W_{1}\) = 0.30, \(W_{2}\) = 0.35, \(W_{3}\) = 0.35 & Increase YF weight & 90.2 \\
3 & \(W_{1}\) = 0.35, \(W_{2}\) = 0.35, \(W_{3}\) = 0.30 & Balanced BF and YF weights & 91.7 \\
4 & \(W_{1}\) = 0.40, \(W_{2}\) = 0.30, \(W_{3}\) = 0.30 & Increased BF (optimal) & 95.6 \\
5 & \(W_{1}\) = 0.45, \(W_{2}\) = 0.25, \(W_{3}\) = 0.30 & Larger BF weight & 93.8 \\
6 & \(W_{1}\) = 0.40, \(W_{2}\) = 0.35, \(W_{3}\) = 0.25 & Slight increase in YF & 94.2 \\
7 & \(W_{1}\) = 0.40, \(W_{2}\) = 0.25, \(W_{3}\) = 0.35 & Slight increase in NF & 93.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Iterative optimization of weight combinations and corresponding fatigue detection accuracy. to drop to 93.8%. This suggests that while BF plays a significant role in fatigue detection, its weight cannot be increased indefinitely to improve performance. Beyond a certain point, increasing BF's weight reduces the influence of other variables, such as YF and NF, leading to a decrease in overall accuracy. When the weights of YF and NF were slightly adjusted in Steps 6 and 7, the detection accuracies were 94.2% and 93.1%, respectively. This indicates that while YF and NF contribute to fatigue detection, their impact on improving detection accuracy is more limited compared to BF. These experiments validate the importance of BF in fatigue detection and reveal its non-linear effect on detection performance. The optimal weight combination (\(W_{1}=0.4\), \(W_{2}=0.3\), \(W_{3}=0.3\)) was achieved by fine-tuning the weights of BF, YF, and NF, representing the best configuration for this task. ## 4 Conclusions

In this article, a multi-feature fusion fatigue detection algorithm that accounts for individual driver characteristics is proposed. By analyzing facial data, the algorithm employs personalized thresholds to determine the states of the eyes and mouth. The algorithm evaluates BF, YF, and NF as evaluation indexes, employing a sliding window model to track dynamic changes in fatigue levels. Unlike fatigue detection algorithms based solely on a single feature, our approach comprehensively considers various facial features associated with fatigue behavior, including the eyes, mouth, and head. This comprehensive approach enhances judgment accuracy and response speed. Experimental results validate the accuracy of the algorithm, with an average accuracy of 95.6%. Moreover, the algorithmic model successfully distinguishes between different levels of fatigue, such as Mild, Moderate, or Severe. Although the proposed method demonstrates high detection accuracy in the experiments, it still has the following limitations:

1. The algorithm is sensitive to variations in lighting intensity, and detection accuracy decreases under extreme lighting conditions (e.g., overly strong or weak light). 2. The initial calibration of personalized thresholds depends on baseline data from the participants, which may limit large-scale application. 3. The feature weight settings are based on manual experience; future work could incorporate machine learning techniques to adaptively optimize the weights. Future research will aim to overcome these limitations by introducing more robust image preprocessing algorithms (such as illumination normalization techniques) and advanced feature fusion models (such as deep learning). In addition, expanding the sample size and increasing participant diversity--covering a broader range of ages, genders, and facial structures--as well as validating the algorithm across multiple publicly available fatigue detection datasets, are essential steps to further improve the model's generalizability and practical applicability. Conceptualization, M.Z. and C.C. ; methodology, M.Z.