## 2 Related Work
### Existing Datasets
The videos encompasses three distinct behaviors: alertness, low vigilance, and fatigue. The recordings were captured from different camera angles and conducted across various scenarios, including working at computer stations, simulated or actual driving environments, and simulated crane operations. The participants exhibited diverse facial characteristics, behaviors, and ethnic backgrounds. The authors of [35] introduced a multimodal database called DROZY. This database was constructed by using multimodal data obtained from 14 participants, who underwent three consecutive sessions of the authors' version of psychomotor vigilance tests (PVTs) proposed by [36] under conditions of escalating sleep deprivation. This test produces an objective measure of vigilance and, indirectly, of drowsiness. For each participant and each PVT session, the database includes time-synchronized raw data, encompassing Karolinska Sleepiness Scale (KSS) measurements [19], polysomnography (PSG) signals, PVT data (including reaction times), and near-infrared (NIR)-intensity and -range images of the face. The creators of the FatigueView dataset [37] introduced a comprehensive, large-scale multi-camera dataset tailored for investigating drowsiness detection in real-world driving scenarios. The dataset includes recordings from both RGB and infrared (IR) cameras positioned at five different angles, capturing genuine instances of drowsy driving along with various visual indicators of drowsiness, ranging from subtle to overt. It encompasses 17,403 distinct instances of yawning across more than 124 million frames sourced from 95 participants. The SUST-DDD dataset [38] comprises 2074 videos captured by participants by using their mobile phone cameras while experiencing fatigue and normal states during authentic driving scenarios. Notably, participants were not directed to exhibit specific behaviors while driving, thereby ensuring the authenticity and safety of the driving experience. Nineteen participants, aged between 24 and 42, were tasked with recording videos by using their personal phones positioned in front of the driver's seat when they felt drowsy or normal. The videos were recorded in periods of reduced daylight, such as driving to or from work. In Table 2, we present the highlighted features of the above-mentioned datasets. It is evident that these datasets offer a considerable number of videos showcasing diverse facial characteristics, behaviors, ethnic backgrounds, lighting conditions, recording settings, and facial poses. Nonetheless, an observable trend unfolds, that is, the majority of existing datasets prioritize the simulation of driving scenarios. This emphasis consequently leads to an insufficiency of recordings that portray tasks associated with computer operation. This gap is particularly pertinent, as such tasks constitute the routine activities of numerous employees and students who may also experience fatigue and drowsiness. Moreover, upon analysis of datasets capturing tasks associated with office work or studying, it becomes evident that they lack a substantial number of informative features related to fatigue, such as vital signs, head movement, and the states of the eyes and mouth. Hence, the detection of these features is mandatory for the implementation of a fatigue recognition system using these dataset, which could be both computationally intensive and time-consuming. To address this limitation, we introduce our dataset, designed to rectify this deficiency by offering a more comprehensive collection of recordings relevant to the daily tasks of employees and students annotated with fatigue related features estimated by deep learning and computer vision methods. Such a dataset holds promise for facilitating fatigue detection research, given its direct relevance to work and academic settings, where fatigue significantly influences work performance and efficiency.