###### Abstract
EEG, ECG and EOG were selected for fatigue detection in [20]. It is fully proved that the fatigue detection performance of more features is better than that of fewer features. The dynamically changing features of the mouse are also currently used for the detection of physiological states associated with people, such as stress [21; 22], mood [23; 24], attention [19; 25] or fatigue [26]. Changes in mouse velocity and distance are used to detect worker fatigue during human-computer interaction. This method has been shown to be able to quantify the fatigue state of workers for a long time without any interference in work [26]. At present, the dynamic features of mouse are mainly used for the detection of physiological states such as emotion and attention, and there are few studies on fatigue. Our goal is to achieve real-time and high-precision non-invasive fatigue status detection. The research in this paper is focused on machine learning. In [27], it is summarized that the models used for the implementation of fatigue detection techniques include mathematical model-based, rule-based and machine-learning-based implementation methods. The current detection method used for main control operators in nuclear power plants is mainly through statistical analysis [7; 8; 9]. In [28] a machine learning clustering approach is used to evaluate operator fitness for duty based on operator fatigue status, but it cannot be used to detect fatigue in real time. Deep learning of complex models requires a lot of time for recognition. Traditional machine learning has low equipment requirements and is capable of achieving fast fatigue status recognition. We need to detect the level of operator fatigue in real time. We choose support vector machines (SVM), K-Nearest Neighbors (KNN) and random forests (RF) as machine learning algorithms to identify fatigue [13; 29]. The use of machine learning for fatigue detection has become very widespread. The data used for training and testing in the existing studies are eye-related and mouse-related features. Before supervised learning, it is necessary to segment the sample data and determine the fatigue level and labeled data. These sample data with fatigue level labels are used for training and evaluation algorithms [30]. The methods of data labelling can be broadly classified as follows, using subjective assessment methods [10; 31], time-on-task phases [32] and trends in physiological changes [33]. The data labeling method of a single indicator is always unconvincing. Subjective evaluation depends on the ideas of the evaluators themselves. The time-on-task phases can easily confuse samples between two neighboring time-on-task phases and the trend of physiological changes cannot truly reflect the differences between different users. In 2017, Toeplitz Inverse Covariance-Based Clustering (TICC) was proposed [34]. The TICC clustering method cannot only find repeated patterns in the dataset, but also explain the clustering results. TICC was successfully used to cluster fatigue sensitive indicators in [30]. These fatigue-sensitive indexes include eye index, task performance index and subjective rating index. In nuclear power plants and many other human-computer interaction tasks, they use features such as blink rate or PERCLOS to detect and analyze fatigue. Blink rate and PERCLOS were shown to reflect the level of operator fatigue [9; 35; 36]. In [37; 38], there is a negative correlation between mouse velocity change and operator fatigue. Previous studies have labeled fatigue levels into two categories: fatigue and non-fatigue. Fatigue is a process with complex physiological and psychological changes and accumulation. Fatigue occurs when cognitive load accumulates to a certain level [9; 39; 40; 41]. Multi-level fatigue labeling helps improve our understanding of operator fatigue, providing a basis for developing techniques to prevent operators' fatigue operation in an MCR. In order to study the real time fatigue detection, an appropriate time window means a lot.