## 2 Related Work
### Existing Approaches to Human Fatigue Assessment  
In this section, we detail the proposed approaches and methodologies employed for mental fatigue assessment for drivers and operators, since the biggest focus in the existing literature is on the mental fatigue caused by the driving situation. The article [14] proposes a lightweight neural network model designed to address driver fatigue detection. The model comprises two primary components: object detection and fatigue assessment. Initially, a lightweight object detection network is developed to rapidly identify the opening and closing states of the driver's eyes and mouth in the video. Following this, the EYE-MOUTH (EM) driver fatigue detection model encodes these states and computes two critical metrics: the Percentage of Eyelid Closure over the Pupil (PERCLOS) and the Frequency of Open Mouth (FOM). Finally, a multi-feature fusion judgment algorithm is employed to assess the driver's fatigue state. The achieved accuracy was 98.30%. Other researchers [15] introduced a system aimed at detecting drowsiness in drivers, featuring an architecture specifically designed to recognize signs of fatigue. The system incorporates four deep learning models, AlexNet, VGG-FaceNet, FlowImageNet, and ResNet, which analyze RGB video footage of drivers to facilitate drowsiness detection. The architecture focuses on four distinct feature categories: hand gestures, facial expressions, behavioral traits, and head movements. AlexNet is utilized to manage variations in environmental conditions, while VGG-FaceNet extracts facial features such as gender and ethnicity. Flow-ImageNet is responsible for assessing behavioral traits and head movements, and ResNet specializes in detecting hand gestures. The system classifies the detected features into four categories: alert, drowsy with eye blinking, yawning, and nodding. The outputs from these models are processed through an ensemble algorithm, culminating in a final classification via a SoftMax classifier. This system obtained an accuracy of 85%. The authors of [16] utilized a pre-trained model employing Histogram of Oriented Gradients (HOG) [17] in conjunction with a linear Support Vector Machine (SVM) to extract the positions of the eyes, nose, and mouth, while also calculating the eye aspect ratio (EAR), mouth opening ratio (MOR), and nose length ratio (NLR). Once these three features are determined, drowsiness detection is performed on the extracted frames. Initially, adaptive thresholding is applied for the classification of blinking, yawning, and head tilting, followed by the implementation of various machine learning algorithms to further categorize the data into drowsy and non-drowsy. The SVM demonstrated the highest performance, achieving an accuracy of 96.4%. Another proposed framework that facilitates drowsiness detection by examining the driver's facial features captured through a camera was presented in [18]. The initial step involves face detection, where the driver's face is identified within a video frame by utilizing Histogram of Oriented Gradients (HoG) features in conjunction with a Support Vector Machine (SVM). Subsequently, specific facial landmark positions are determined. These landmarks assist in estimating head orientation and identifying instances of blinking. By systematically monitoring blink duration, blink frequency, and PERCLOS (Percentage of Eyelid Closure over the Pupil over time), the level of driver drowsiness is assessed through a Fuzzy Inference System (FIS). The performance of the proposed system was tested against the subjective evaluation of the driver based on Karolinska Sleepiness Scale (KSS) [19], with an average normalized root mean square error (NRMSE) of 15.63%. The study [20] introduces a real-time method for monitoring driver disturbances by utilizing Convolutional Neural Networks (CNNs). The detection process employs advanced CNN architectures, including InceptionV3, VGG16, and ResNet50.