## 4 The Proposed Approach and Its Application to the HFAVD Dataset
Additionally, this limitation stems from the scarcity of public datasets addressing mental fatigue among operators in work and academic contexts, highlighting a significant gap that our study aims to fill, encouraging further research efforts to address this. Nevertheless, the only approach that allows for a valid comparison is [28], as it utilized the same dataset. However, our approach demonstrates superior performance by achieving higher accuracy through the use of physiological features rather than relying on eye-tracking data. Additionally, it is noteworthy that many existing approaches primarily rely on features related to the eyes and mouth. However, factors such as the use of sunglasses, reflections from lighting on eyewear, or mouth coverings for medical purposes can affect the detection accuracy. In contrast, our method incorporates a diverse range of parameters that account for both the internal and external states of the subject, thereby enhancing the robustness of our approach in managing these challenges. Furthermore, the majority of the existing datasets rely on subjective indicators to evaluate fatigue levels among participants. These subjective measures can be influenced by personal perceptions and biases, which may compromise the validity of the findings. On the other hand, our study adopts a more robust approach by employing an objective metric to assess fatigue. This choice not only minimizes potential biases but also enhances the overall reliability and validity of our results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Features** & **Used Features** & **Accuracy** & **F1-Score** \\ \hline \multirow{2}{*}{All 13 features} & BP Diastolic, participant ID, BP Systolic, eye\_closure\_ratio, heart\_rate, oxygen\_saturation, average & \multirow{2}{*}{98.1\%} & \multirow{2}{*}{0.8658} \\  & pitch, average roll, mouth\_openess\_ratio, average yaw, average RR, rhythmicity\_coef, and & & \\  & stability\_coef & & \\ \hline \multirow{2}{*}{Top 11 features} & BP Diastolic, participant ID, BP Systolic, eye\_closure\_ratio, heart\_rate, oxygen\_saturation, average & \multirow{2}{*}{98.27\%} & \multirow{2}{*}{0.8748} \\  & pitch, average roll, mouth\_openess\_ratio, average yaw, and average RR & & \\ \hline \multirow{2}{*}{Top 9 features} & BP Diastolic, participant ID, BP Systolic, eye\_closure\_ratio, heart\_rate, oxygen\_saturation, average & \multirow{2}{*}{98.43\%} & \multirow{2}{*}{0.8891} \\  & pitch, average roll, and mouth\_openess\_ratio & & \\ \hline \multirow{2}{*}{Top 7 features} & BP Diastolic, participant ID, BP Systolic, eye\_closure\_ratio, heart\_rate, oxygen\_saturation, and & & \\  & average pitch & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of XGBoost model with varying number of features. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **F1-Score with Participant ID** & **F1-Score Without Participant ID** \\ \hline Random forest & 0.9465 & 0.8190 \\ Logistic regression & 0.52147 & 0.4788 \\ XGBoost & 0.8658 & 0.8128 \\ Decision tree & 0.8256 & 0.7237 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of F1-scores for different classification methods with and without the participant ID feature.