## 4 Discussion and Conclusions  
It is found that TICC can objectively determine the fatigue level by using the change trend of subjective evaluation indicators (KSS and SSS scales), eye indicators (BR and PERCLOS) and mouse indicators (AMV and AOV within the sampling time). In the human-computer interaction environment of the main control room of a nuclear power plant, the results show that several widely used classification algorithms can perform fatigue detection with different feature sets. All have achieved good results. In particular, KNN shows excellent performance in various feature sets and the accuracy rate is above 85%. We find through experiments that, in a sliding window of 60 s and a sliding step \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Feature Set**} & \multicolumn{4}{c}{**Classification Performance (\%)**} \\ \cline{3-6}  & & **Accuracy** & **Macro-P** & **Macro-R** & **Macro-F1** \\ \hline \multirow{4}{*}{SVM} & 1 & 78.54 & 74.70 & 68.32 & 70.49 \\  & 2 & 80.85 & 76.77 & 71.02 & 73.33 \\  & 3 & 87.42 & 84.91 & 82.05 & 83.23 \\ \cline{1-1}  & 1 & 83.29 & 78.32 & 78.60 & 78.32 \\ \cline{1-1}  & 2 & 86.71 & 82.54 & 82.87 & 82.60 \\ \cline{1-1}  & 3 & **91.83** & **89.71** & **88.86** & **89.16** \\ \cline{1-1}  & 1 & 83.57 & 83.97 & 71.99 & 76.28 \\ \cline{1-1} RF & 2 & 85.49 & 87.24 & 73.58 & 78.40 \\ \cline{1-1}  & 3 & 89.25 & 91.01 & 81.15 & 85.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Classification performance of different classification algorithms with different feature sets. Figure 5: Confusion matrix corresponding to the three types of feature sets of the three algorithms. of 10 s, the accuracy of the three algorithms is relatively high. Our model can basically identify the fatigue state of the operator in about 10 s, except for the initial 60 s data. In comparing the performance of the eye and mouse features we find that previous studies have illustrated that eye-related features have very promising recognition performance in fatigue detection, but our research proves that the combination of mouse features and eye features can detect fatigue more accurately. From the clustering results, it can also be obtained that the subjective scores of operators increase. Blink frequency and PERCLOS also increase. The movement velocity (AMV and AOV) of the mouse is reduced instead. It is confirmed that the characteristic changes of eyes and mouse are consistent with the change trend of fatigue in [9; 30; 37]. Our research is making efforts to solve the problem of real-time detection of operator fatigue in the main control room of nuclear power plants and help develop an operator fatigue detection method for nuclear power plants. However, we still need to do some further work. First, our study does not consider the effect of different simulation tasks on the data, so there may be different physiological and behavioral changes for fatigue states in different experimental scenarios, such as accident scenarios and normal start-stop pairs of scenarios. Second, our experimenters are all graduate students. Although our subjects all spent more than eight hours learning, there is still some difference compared to real plant operators. By comparing the results of this experiment, we can understand that multiple features are more effective than a single feature. Possible future work will be to extract more features manually or to use more non-invasive devices to obtain fatigue indicators. The results and methodology of this study lay the groundwork for further explorations into mental fatigue in NPP MCR environments and could be applied to develop a reliable in MCR fatigue detection system in restricted NPP. Conceptualization, Y.L. and L.D. ; methodology, Y.L. ; software, Y.L. ; validation, Y.L. ; formal analysis, Y.L. ; investigation, Y.L. and L.D; data curation, Y.L. and M.Z. ; writing--original draft preparation, Y.L. and L.D. ; writing--review and editing, Y.L. and L.D. ; visualization, Y.L. ; supervision, L.D.