## 2 Algorithm and Methodology
### Sliding Window Model
Upon review of the dataset, certain conditions were found to be unfavorable for detection, such as talking, laughing, yawning with the mouth covered, facial obstructions, and extreme lighting conditions (either too strong or too weak), all of which reduce detection accuracy. Therefore, future research may focus on improving fatigue detection under these challenging conditions. However, based on the experimental \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Yawns MAE** & **Yawns RMSE** & **Blinks MAE** & **Blinks RMSE** \\ \hline Personalized Threshold & 0.58 & 0.76 & 2.50 & 2.71 \\ Average Threshold & 1.42 & 1.55 & 9.25 & 10.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of MAE and RMSE for yawn and blink detection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. ** & **YawDD (Video ID)** & \begin{tabular}{c} **Number of Blinks** \\ **(Detection/Actual)** \\ \end{tabular}  & \begin{tabular}{c} **Number of Yawns** \\ **(Detection/Actual)** \\ \end{tabular}  & \ \begin{tabular}{c} **Number of Nods** \\ **(Detection/Actual)** \\ \end{tabular}  & \begin{tabular}{c} **Fatigue Status** \\ **(Detection/Actual)** \\ \end{tabular}  \\ \hline
1 & 1-FemaleNoGlasses-Yawning.avi & 6/7 & 1/1 & 0/0 & Y/Y \\
2 & 6-FemaleNoGlasses-Yawning.avi & 6/6 & 2/2 & 1/1 & Y/Y \\
3 & 10-MaleNoGlasses-Normal.avi & 5/7 & 2/2 & 0/0 & Y/Y \\
4 & 44-MaleNoGlasses-Yawning.avi & 8/10 & 2/2 & 1/1 & Y/Y \\..



results, the overall detection accuracy remains high, which does not affect the practicality and reliability of the proposed method. ### Comparison with Benchmark Methods

To objectively evaluate the effectiveness and advantages of the proposed multi-feature fusion fatigue detection method, comparative experiments were conducted against existing benchmark methods from the relevant scientific literature. Experimental results from these methods and the proposed algorithm are summarized in Table 6.

As shown in Table 6, the proposed method achieves the highest accuracy (95.6%) among all compared approaches. Traditional methods without personalized thresholds, although integrating one or more fatigue indicators, generally result in slightly lower performance. For example, the method by [PERSON] et al. [39] reached only 87.37%, while [PERSON] et al. 's [22] method achieved 95.10% but did not consider nodding behavior. By incorporating blinking, yawning, and nodding frequencies along with a personalized threshold strategy, the proposed approach demonstrates superior detection accuracy. ### Impact of Weight Combinations on Fatigue Detection Accuracy

This section investigates the impact of different weight combinations for blinking frequency (BF), yawning frequency (YF), and nodding frequency (NF) on the accuracy of fatigue detection. As shown in Table 7, the weight distribution across multiple experimental steps was optimized to understand how changes in feature weights affect detection performance. The experiment began with an initial setup (Step 1) that was biased towards NF, resulting in a detection accuracy of 88.9%. The weight of YF was then increased (Step 2), making YF more influential in the detection process, which led to an increase in accuracy to 90.2%. In Step 3, the weights of BF and YF were balanced, and the accuracy further increased to 91.7%. However, when the weight of BF was increased to 0.4 in Step 4, the highest accuracy of 95.6% was achieved. Increasing the BF weight further in Step 5 to 0.45 caused the accuracy \begin{table}
\begin{tabular}{l l l c} \hline \hline
**Method Reference** & **Indicators Used** & **Personalized Threshold** & **Accuracy (\%)** \\ \hline [PERSON] et al. [39] & EAR, MAR & No & 87.37 \\ [PERSON] et al. [40] & PERCLOS, POM & No & 93.62 \\ [PERSON] et al. [41] & Eye closure, Yawning & No & 94.31 \\ [PERSON] et al. [42] & Eye features (EAR, PERCLOS, BF, POR) & No & 95 \\ [PERSON] et al. [22] & BF, YF & No & 95.10 \\ Proposed Method & BF, YF, NF & Yes & 95.6 \\ \hline \hline \end{tabular}
\end{table}