## 4 The Proposed Approach and Its Application to the HFAVD Dataset
Notably, when training a random forest classifier without including the participant ID, the model achieved an F1-score of 0.7990 by using the original 15 features. Subsequent removing the redundant features, identified through correlation analysis, led to an improvement in the F1-score to 0.8384, highlighting the significance of feature selection in enhancing classification performance. This finding underscores the importance of addressing feature redundancy to optimize model performance in classification tasks. Another crucial aspect is analyzing the most relevant characteristics for classification. Given the numerous variables used to characterize the problem, it is essential to determine their relevance and impact on classification performance. The relevance of each feature was analyzed, revealing that the most to least relevant features are BP Diastolic, participant ID, BP Systolic, eye_closure_ratio, heart_rate, oxygen_saturation, average pitch, average roll, mouth_openess_ratio, average yaw, average RR, rhythmicity_coeff, and stability_coef. The experiments shown in Table 6 revealed that using all 13 features resulted in an accuracy of 98.1% and an F1-score of 0.8658. Reducing the number of features to 11 increased the accuracy to 98.27% and the F1-score to 0.87481. A further reduction to nine features improved the accuracy to 98.43% and the F1-score to 0.8891. However, using only seven features slightly decreased the accuracy to 98.1% and dropped the F1-score to 0.8597. Leaving less than seven features produces significantly worse results. These results indicate that reducing the number of features can enhance classification performance up to a point, highlighting that not all features are equally relevant. Thus, focusing on the most significant features can enhance the model's efficiency and accuracy. Another notable observation is that the high importance of the participant ID feature means that the fatigue assessment models tend to be person-dependent. Table 7 shows the drop in the methods' performance after dropping the participant ID feature. The participant ID's role in classification processes is underscored by a set of research observing differential reactions to changes in mental fatigue states among subjects, as evidenced in alterations in vital signs and eye movement. In one study, ref. [48] discovered that exposure to a fatigue-related task led to an increase in blood pressure in some participants, while others registered a decrease. This observation extended to respiratory rates, reinforcing the individual variance in physiological responses to fatigue. Moreover, ref. [49] found a correlation between mental fatigue and eye movement data (including blink frequency) in three of their subjects, as captured by an eye detection system, while the rest of the subjects did not show any significant relationship. These findings underscore the crucial nature of participant ID as a parameter. It enables machine learning models to account for person-specific dependencies, thereby enhancing their predictive accuracy for fatigue levels. Thus, the participant ID emerges as a significant factor in optimizing classification processes by accommodating individual variability. Table 8 presents a comparative analysis of existing methodologies for detecting fatigue through images or videos without the use of sensors or specialized equipment. While our approach demonstrates good accuracy (98.43%), it is important to note that the dataset employed is relatively small compared with those utilized in other studies, such as [22]. The mentioned accuracy rates in the table were calculated based on different data, where fatigue was identified based on different metrics (subjective, objective, or based on monitoring some features); hence, the approaches listed cannot be compared directly. Nevertheless, the accuracy of our models is high enough for the proposed models to be used for fatigue detection.