## 3 Dataset Description
### Data Example  
As previously stated, inclusive CSV files were generated to meticulously record all the details of each video within the OperatorEYEVP dataset. In this subsection, we provide a thorough description of these files, elucidating the significance of each feature included therein, as well as their intended purpose. Additionally, we provide examples of how these files could potentially be utilized by other researchers. For each participant, a CSV file encompassing 199 columns was generated to provide an extensive description of the session performed by the subject (see Table 3). The initial columns within the file include metadata associated with the session, including participant ID, session name, the type of activity undertaken, and the time of day during which the session occurred. Subsequently, information pertaining to the CRT sessions, specifically the first and second sessions, is listed. This information includes metrics such as the average reaction time, standard deviation of reaction time, and the number of errors made. Following that, we included the pertinent information related to the Landolt rings test, encompassing a variety of indicators and metrics. This includes many indicators about attention, stability of attention and work accuracy, the quantification of mental productivity through a mental productivity index, and the evaluation of mental performance. Additionally, we incorporated a concentration indicator, the qualitative characteristic of concentration, the capacity of visual information processing, and the processing rate. Furthermore, we listed additional information related to the test results, such as the total number of characters crossed out, letters correctly selected, letter characters missed, wrongly selected characters, total number of lines viewed, and other relevant variables. Subsequent to this, we added a collection of features related to eye movement strategy (pupil movement) observed during each session. These features, totaling 60 in number, encompass measurements related to fixation and saccadic time and velocity, alongside additional parameters associated with acceleration. Additionally, an extensive set of 79 parameters were included, encompassing various domains such as time, frequency, and nonlinearity, all of which are related to heart rate variability. Notable examples within this set include VLF, LF, and HF band frequencies, as well as relative and absolute power measurements for these frequency bands, among numerous other relevant metrics. In addition, we extracted the absolute heart rate for each minute from the PPI signal provided. We thought that it would be useful to include both real and predicted heart rate (by our model (Section 3.3.2)), so that many experiments and comparisons may be carried out by researchers to evaluate the possibility of integrating and relying on computer science for such tasks. All the aforementioned features were provided by [13]. The next stage involved extracting the relevant features from the x- and y-coordinates from the eye movement data, which were represented as two separate time series for each activity. To accomplish this, we employed basic Python (v. 3.6.9) code in conjunction with the NumPy library to compute seven key features from each series, namely, mean, standard deviation, minimum, maximum, 25 th percentile, 50 th percentile, and 75 th percentile. Finally, we added the 15 features extracted by our models after processing the videos from the OperatorEYEVP dataset. These features included respiratory rate, rhythmicity and stability of breathing, eye closure ratio, mouth opening ratio, head Euler angles (roll, pitch, and yaw), the ratio of frames where any Euler angle exceeds 30 degrees relative to the total frames in one minute (angles \(>\) 30), the count of eye closures lasting more than 2 s (count of eye closure \(>\) 2 s), the count of yawns, heart rate, systolic and diastolic blood pressure, and blood oxygen saturation.