## 4 The Proposed Approach and Its Application to the HFAVD Dataset  
In this section, we delve into the experiments used to analyze the dataset, along with presenting the resulting outcomes. The dataset provided was utilized to detect fatigue levels based on the psychological traits extracted by our developed models, as explained in Section 3.2. The overall framework is illustrated in Figure 3.  
As illustrated in the diagram, our approach involves extracting key physiological indicators from the videos of operators using a set of specialized models developed for this purpose. These indicators include heart rate, respiratory rate, blood pressure, oxygen saturation, eye closure ratio, head pose, and yawning frequency. Each of these physiological signals is calculated on a per-minute basis, ensuring a continuous, time-resolved input into the fatigue detection process. Once these indicators are extracted, they are used as input features for our fatigue detection model. This model is designed to assess the operator's fatigue state based on the mental performance (AU), which is derived from the results of the Landolt rings test--a well-known measure of visual acuity and attention. The Landolt  
Figure 3: The overall scheme used for detecting the fatigue state. test provides insights into the operator's cognitive state, specifically focusing on attention and concentration. To determine the optimal threshold for AU, which separates the fatigue state from the non-fatigue state, we conducted a detailed analysis of the data. This analysis involved evaluating the mental performance across different AU values to identify the point at which fatigue became evident. In addition to AU, we also analyzed two other important cognitive measures: the attention level (K) and the stability of concentration (Ku). These metrics helped us gain a more comprehensive understanding of how attention and focus degrade over time, particularly as fatigue sets in. We employed the results of the Landolt test, particularly mental performance, to assess the fatigue level due to its capacity to effectively capture the cognitive and attentional aspects associated with fatigue. Unlike subjective measures, such as the Visual Analog Scale for Fatigue (VAS-F), which rely on self-reported perceptions, which can be swayed by individual biases and emotional states, AU provides a more objective and standardized evaluation of fatigue. Table 4 shows the mental performance (Au), attention (K), and the stability of concentration of attention (Ku) for each participant. Given that attention decreases when a person is fatigued, we can draw meaningful insights from our data. For example, participant number 6 has the attention level above 60, which is considered good [46]. That indicates that the participant 6 is not fatigued. Therefore, we can conclude that the fatigue threshold for mental performance must be below 0.59, as individuals with mental performance scores above this threshold maintain high attention levels. To predict the fatigue state, we based our study on the hypothesis that mental performance becomes lower with the increase in fatigue. Figure 4 illustrates the relationship between mental performance (as measured by the Landolt rings test) and the inverse of fatigue (1/Fatigue). We assume that there is a threshold value (a point or a small range of points) where fatigue level change causes a significant change in physiological indicators. We also assume the this change can be captured by a machine learning model. We assessed each participant's mental performance, classifying them as fatigued if their performance fell below a predetermined threshold. To identify the optimal threshold, we conducted multiple experiments by using the random forest algorithm, testing thresholds from 0.2 (chosen empirically from our dataset) to 0.59 (discussed above) in increments of 0.015. This increment value was chosen experimentally, as performance differences were negligible with smaller increment values.