###### Abstract  
The detection of the human mental fatigue state holds immense significance due to its direct impact on work efficiency, specifically in system operation control. Numerous approaches have been proposed to address the challenge of fatigue detection, aiming to identify signs of fatigue and alert the individual. This paper introduces an approach to human mental fatigue assessment based on the application of machine learning techniques to the video of a working operator. For validation purposes, the approach was applied to a dataset, \"Human Fatigue Assessment Based on Video Data\" (HFAVD) integrating video data with features computed by using our computer vision deep learning models. The incorporated features encompass head movements represented by Euler angles (roll, pitch, and yaw), vital signs (blood pressure, heart rate, oxygen saturation, and respiratory rate), and eye and mouth states (blinking and yawning). The integration of these features eliminates the need for the manual calculation or detection of these parameters, and it obviates the requirement for sensors and external devices, which are commonly employed in existing datasets. The main objective of our work is to advance research in fatigue detection, particularly in work and academic settings. For this reason, we conducted a series of experiments by utilizing machine learning techniques to analyze the dataset and assess the fatigue state based on the features predicted by our models. The results reveal that the random forest technique consistently achieved the highest accuracy and F1-score across all experiments, predominantly exceeding 90%. These findings suggest that random forest is a highly promising technique for this task and prove the strong connection and association among the predicted features used to annotate the videos and the state of fatigue. operator fatigue detection; computer vision; physiological indicator; machine learning Article  
Human Operator Mental Fatigue Assessment  
[PERSON], [PERSON], [PERSON], A. Human Operator Mental Fatigue Assessment Based on Video: ML-Driven Approach and Its Application to HFAVD Dataset  
Numerous solutions have been proposed to tackle the issue of fatigue detection, with the primary aims of identifying signs of fatigue and subsequently alerting the concerned individual to this hazardous state. The majority of these methodologies employ machine learning classifiers or leverage deep learning models to construct a high-efficacy system for fatigue detection [5]. Most of the research on fatigue monitoring has concentrated on identifying fatigue during cognitive tasks, such as driving [6], and some researchers dedicated their endeavors to identifying signs of fatigue in natural-viewing situations, where individuals are not actively engaged in cognitive tasks [7]. Non-intrusive techniques, including those that utilize remote or webcam-based eye tracking, generally monitor variations in an individual's pupil response, blink rate, and eye movement patterns to deduce signs of fatigue during such cognitive tasks [8]. Within the fields of machine learning and deep learning, the selection of the training dataset is a critical factor that influences several aspects of the model, including its accuracy and generalizability. Consequently, numerous datasets have been curated, developed, and utilized for the advancement of fatigue or drowsiness detection techniques [5]. Some of these datasets rely on the use of sensors and devices to collect pertinent features associated with an individual's fatigue state and mental cognition. These include eye trackers [9], electroencephalogram (EEG) signals [10], and electrocardiography (ECG) signals [11], as well as other physiological indicators. However, the evolution of data collection techniques has led to the creation of more sophisticated datasets. These datasets comprise videos of drivers or operators engaged in a task or driving. The availability of such datasets opens up new avenues for experimentation, particularly with deep learning models.