## 3 Dataset Description
### Data Example
The final column in the dataset represents the subjective assessment of fatigue, specifically measured by using the Visual Analogue Scale \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Features** & **Features** & **Description** \\ \hline Info about the video & Four features included (participant ID, video name, time of day, and type of activity) & General info about the participant and the session \\ \hline Questionnaire & One value presents VAS-F result & Subjective assessment of fatigue performed by the participants themselves \\ \hline Task results & 6 features describe CRT results & Metrics related to the performance during the CRT sessions \\
19 features related to Landolt rings test results & Metrics related to the performance during the Landolt rings \\ \hline  & 60 eye movement strategy features & Eye characteristics regarding saccadic and fixation time \\  & 14 eye movement statistics features & Mean, standard deviation, minimum, maximum, 25 th \\ Sensor features & 14 eye movement statistics features & percentile, 50 th percentile, and 75 th percentile of each series \\  & & on both coordinates \\  & 79 HRV features & Features related to the HRV in multiple domains (time, frequency, and nonlinear) \\  & One value presents the actual heart rate & Real heart rate extracted from the PPI signals \\ \hline Features extracted & 15 features, including head movement angles (roll, pitch, and yaw), vital signs (blood pressure, respiratory rate, heart rate, and oxygen saturation), and eye and mouth states (eye closure and yawning) & Detected features from each video at each minute using deep learning models proposed in our previous \\  & & works [39; 40; 42; 43; 44; 45] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The contents of the CSV files. for Fatigue (VAS-F). The participants themselves provided this assessment, indicating their perceived level of fatigue. We believe that our contribution to the OperatorFYEVP dataset offers a valuable opportunity for researchers to enhance their understanding of the relationship between mental fatigue and various responses exhibited by subjects. These responses encompass both observable cues, such as head movement and eye and mouth state, as well as underlying indicators, such as vital signs. Researchers can gain insights beyond fatigue alone by exploring the correlations between these responses and multiple features, including attention, work accuracy, concentration stability, and mental performance. We consider that our previous approaches and research have been employed in an optimal manner. The advantage of utilizing these approaches lies in their ability to provide access to all the aforementioned features without the need for sensors or external devices. This not only saves considerable time and effort in conducting studies but also eliminates the need to develop costly approaches to detecting specific features. Consequently, researchers can promptly apply and test their hypotheses and theories. Furthermore, the availability of annotating videos capturing various activities, not solely limited to fatigue, can provide a deeper understanding of the human mechanism.