###### Abstract
These models can process video data to recognize signs of fatigue with no need for any additional devices for the individual. Such non-intrusive methods enhance the applicability and usability of these approaches in safety and monitoring systems, thereby offering potential solutions for real-world fatigue detection challenges. In this article, we present a selection of datasets that offer various advantages, particularly in terms of the wide range of the facial characteristics, behaviors, ethnic backgrounds, lighting conditions, recording settings, and facial poses they encompass. However, we also draw attention to a big limitation, that is, a noticeable trend of existing datasets of primarily focusing on simulating driving scenarios, which has created a lack of recordings that depict operator (computer user)-related tasks where fatigue exerts a substantial influence on both work performance and productivity. Therefore, our current research focuses on operator fatigue detection in work and academic settings. In this study, subjects experienced the causes of the two types of mental fatigue [12]. The first type is active fatigue, which arises from prolonged task-related perceptual-motor adjustments; for example, operators reading scientific articles develop cognitive strategies that enhance their ability to process complex information. This adaptation includes adjustments in reading speed and focused attention on key concepts. The second type is passive fatigue, which occurs due to extended periods of inactivity, such as sitting in front of a computer for a long time, leading to mental exhaustion. The originality of the proposal is in the usage of physiological indicators obtained by using computer vision for human fatigue state detection based on facial videos. The videos are taken from the public dataset OperatorYEVP [13]. Therefore, we applied multiple advanced deep learning and computer vision models to analyze the dataset videos. In doing so, we successfully extracted physiological characteristics, which represents a novel and original contribution to the field. Unlike the usage of wearable or other sensors attached to the human body, computer vision techniques can be more useful in many situations, since they are non-intrusive and do not limit human mobility. Our contribution can be summarized as follows:  
1. Innovative approach to non-intrusive fatigue detection: Diverging from conventional sensor-based research on mental fatigue, this study introduces a novel, non-intrusive method using deep learning, computer vision, and machine learning models. Unlike the traditional methods that leverage sensor-based data for mental fatigue detection, we extract features by using a simple camera and apply machine learning techniquesfor fatigue prediction. This significant shift offers practical benefits due to its accessibility and user friendliness, demonstrating the potential of our approach as a valuable tool for fatigue research. 2. Estimation of physiological indicators: We utilize the OperatorEYEVP dataset [13], which contains a diverse collection of videos capturing the daily tasks of employees and students. Through the application of deep learning and computer vision models developed in our previous work, we estimate a set of physiological indicators that provide an evaluation of the subject's physiological condition in these videos. 3. Comprehensive dataset HFAVD: The resulting dataset includes physiological indicators besides other data (Landolt rings test results, VAS_F, and sensor data related to eye tracking and HRV). These indicators comprise head movement (as Euler angles: roll, pitch, and yaw), vital signs, and eye and mouth states (eye closure and yawning), which were automatically calculated and detected by using deep learning models, eliminating the need for manual intervention, which is often required by other existing datasets. 4.