## 2 Related Work
### Existing Approaches to Human Fatigue Assessment
Consequently, the ResNet50 model outperforms the other deep learning architectures, attaining an accuracy of 93.69% with a loss of 0.6931. The paper [21] introduces a cost-effective framework for predicting driver fatigue levels, which aims to detect fatigue in its initial stages. The framework assesses driver fatigue by analyzing behavioral cues related to the eyes, mouth, and head, utilizing an infrared (IR) camera. The features are detected by different architectures of CNNs, and the fatigue classification process is completed by using a logical model. To evaluate the effectiveness of the proposed fatigue prediction framework, experiments were conducted by using real datasets under both nighttime and daytime lighting conditions. The results indicate that the proposed method successfully predicts driver fatigue levels with an overall accuracy of 93.3%. The authors of [22] introduced an algorithm for recognizing fatigue states. The methodology begins with the application of a Multitask Convolutional Neural Network (MTCNN) to detect human faces. Subsequently, DLIB (v.19.19.0), an open-source software library, is utilized to identify facial key points, enabling the extraction of fatigue feature vectors from each frame. These fatigue feature vectors from multiple frames are then concatenated into a temporal feature sequence, which is input into a Long Short-Term Memory (LSTM) network to derive a final fatigue feature value. The accuracy rates of fatigue detection of their method were 88% on the YawDD dataset [23] and 90% on their self-built dataset. The article [24] employed an RGB-D camera to capture both RGB and infrared video of the face. The facial video is analyzed independently by using the Joint Approximate Diagonalization of Eigenmatrices (JADE) algorithm [25], Fourier transform, and Triangular Surface Patch (TSP) descriptor [26] to extract three key fatigue features: heart rate, eye opening level, and mouth opening level. Subsequently, the authors developed a novel Multimodal Fusion Recurrent Neural Network (MFRNN) that integrates these three features to enhance the accuracy of driver fatigue detection. Furthermore, a Recurrent Neural Network (RNN) layer is incorporated within the MFRNN to capture the temporal dynamics of the features. Fuzzy reasoning is combined with the RNN to extract the temporal information of the heart rate to address issues related to fuzziness and noise. The proposed method achieved fatigue detection accuracy rates of 86.75% and 91.67% across two distinct datasets. The study [27] investigated the use of geometric measurements of facial features to assess mental fatigue in construction equipment operators. Seventeen male operators participated in excavation tasks, with data collected consistently over several days during the same morning hours under clear weather conditions. Mental fatigue was evaluated through both subjective NASA-TLX scores and objective electrodermal activity (EDA) recorded via the Empatica E4 sensor. The analysis focused on specific facial features, including the eyebrow, mouth contours, head movements, eye region, and overall facial area. The results revealed significant differences in these metrics between high and low fatigue states, particularly in the eye and facial area measurements, with mean differences of 45.88% and 26.9%, respectively. The findings indicate that geometric facial assessments can serve as an effective, non-invasive method for detecting mental fatigue in this occupational context. The authors of the OperatorYEVP dataset, which will be utilized in our experiments, developed a method for detecting mental fatigue based on human eye movements by using this dataset [28]. Their research was grounded in the analysis of eye-tracking data collected from human operators. Within the framework of the proposed methodology, they introduced a technique for identifying the most pertinent gaze characteristics associated with mental fatigue detection, incorporating various machine learning techniques.