## 2 Related Work
### Existing Approaches to Human Fatigue Assessment
Although these features are known to correlate significantly with fatigue, there are circumstances--such as the presence of sunglasses, reflections from lighting on eyewear, or mouth coverings due to medical reasons--where accurate detection may be compromised. Furthermore, some studies have employed fuzzy logic systems or logical models for fatigue classification. These systems are often hindered by their lack of learning capabilities and their dependence on expert knowledge, as their effectiveness is contingent upon the quality of the rules established by specialists. Conversely, other research has focused on deep learning methodologies, including CNNs and RNNs, which, while powerful, are computationally intensive and necessitate substantial datasets for effective training. Moreover, the metrics used to define the fatigue states were mostly subjective, such as NASA-TLX or data collected from sensors (EDA). Nevertheless, numerous studies investigating operators' mental fatigue have relied on sensors to gather the features utilized in their research. This reliance on sensors may be viewed as a limitation, as these devices can introduce discomfort for the participants involved in the studies. Additionally, a noticeable trend is the exclusive focus on detecting driver fatigue, which overlooks other contexts in which individuals, such as operators and students, may also experience fatigue. This narrow scope may limit the applicability of the findings across broader domains. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Article** & **Used Features** & **Technique Used for Fatigue Detection** & **Accuracy of Fatigue Detection Models** \\ \hline [PERSON] et al. [14] & Eye and mouth features & A multi-feature fusion judgment algorithm & 98.30\% \\ \hline [PERSON] et al. [15] & Hand gestures, facial expressions, behavioral features, and head movements & SoftMax classifier & 85\% \\ \hline [PERSON] et al. [16] & Eye, mouth, and nose features & SVM & 96.4\% \\ \hline [PERSON] et al. [18] & Eye features & Fuzzy logic-based system & NRMSE of 15.63\% \\ \hline [PERSON] et al. [20] & Features detected by CNN layers & CNN (ResNet50) & 93.69\% \\ \hline [PERSON] et al. [21] & Eye, mouth, and head features & Logical model & 93.3\% \\ \hline [PERSON] et al. [22] & Eyes and mouth features & LSTM & 88\% and 90\% \\ \hline [PERSON] et al. [24] & Heart rate, and eye and mouth features & MFRNN & 86.75\% and 91.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of approaches employed for fatigue detection using facial videos. In an effort to address the aforementioned limitations, our study focuses on detecting mental fatigue associated with cognitively taxing tasks. This form of fatigue parallels the exhaustion experienced by students and workers after extended periods of studying or working. Importantly, our approach extends beyond the exclusive reliance on eye and mouth features, incorporating multiple features to enhance fatigue recognition. It should be emphasized that all features employed in this study are derived from pre-trained deep learning models, eliminating the need for additional sensor use or retraining, thus reducing costs. Furthermore, we utilize cost-effective machine learning techniques, such as random forest, for fatigue detection based on these estimated features. This strategy makes our approach cost-effective, straightforward, and user-friendly.