## 2 Related Work
### Existing Approaches to Human Fatigue Assessment
The experimental findings indicated that the most significant characteristics included the average velocity within the fixation area, the average curvature of the gaze trajectory, the minimum curvature of the gaze trajectory, the minimum saccade length, the percentage of fixations lasting less than 150 ms, and the proportion of time spent in fixations shorter than 150 ms. The processing of eye movement data using this method is conducted in real time, achieving a maximum accuracy of 85% and an F1-score of 80%, with the random forest algorithm demonstrating the best performance. The research study reported in [29] identified mental fatigue in operators of a construction site by using flexible headband-based sensors for the collection of raw EEG data, along with deep learning networks. The study involved fifteen operators performing a one-hour excavation task on a construction site. The NASA-TLX score served as the ground truth for assessing mental fatigue, while brain activity patterns were captured by using a wearable EEG sensor. The collected raw EEG data were utilized to develop classification models based on deep learning techniques. The performance of these models, specifically Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and one-dimensional Convolutional Networks, was evaluated. The results indicated that the Bi-LSTM model significantly surpassed the other models, achieving an accuracy of 99.941%. These results underscore the effectiveness of the Bi-LSTM model for wearable sensor-based recognition and classification of mental fatigue, thereby enhancing health and safety measures on construction sites. The article [30] presents an innovative methodology that employs three machine learning models in conjunction with multimodal data fusion to classify mental fatigue states. Data were collected during an excavation operation through the integration of electroencephalography (EEG), electrodermal activity (EDA), and video signals. Specifically, a headband was utilized to acquire EEG data from construction equipment operators, an E4 watch was attached to their wrists to collect EDA data, and a video camera was installed on the interior of the excavator's front screen to capture facial feature information. Furthermore, a questionnaire was administered to gather subjective assessments of mental fatigue. The decision tree model, utilizing multimodal sensor data fusion, demonstrated superior performance compared with other models, achieving an accuracy of 96.2%. The findingssuggest that multimodal sensor data fusion has the potential to facilitate the development of a real-time system for classifying mental fatigue. The paper [31] presents the development of a non-invasive smart cushion designed to assess the mental fatigue of construction equipment operators through the real-time continuous monitoring of heart rate and respiration signals. A laboratory experiment was conducted with twelve participants who performed simulated excavator operations for data collection. Subsequently, relevant time-domain and frequency-domain features of the heart rate and respiration signals were extracted to create a random forest classification model aimed at exploring the relationship between vital signs and self-reported mental fatigue, as measured by the NASA-TLX scale. The results indicated that the random forest model achieved a classification accuracy of 92%. Furthermore, the combination of heart rate and respiration features proved to be more effective than using a single feature for identifying and classifying mental fatigue among construction equipment operators. Table 1 summarizes the various approaches discussed previously, highlighting the features utilized and the techniques implemented for fatigue detection, along with the accuracy achieved in each case. While numerous studies report high performance and favorable outcomes, certain limitations are evident. Notably, many methods rely heavily on features derived solely from the eyes and mouth to ascertain fatigue levels.