## 3 Results
### Supervised Learning Classification Results  
To demonstrate the feasibility of mouse and eye features for fatigue detection, we constructed three feature sets for different combinations of mouse and eye. The results show that the performance of multiple features is better than that of single features. The following are the results obtained from the cross-validation method 5-Fold cross-validation used. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Fatigue** \\ **Level** \\ \end{tabular} } & \multicolumn{2}{c}{**Subject Score**} & \multicolumn{2}{c}{**Eye-Related Feature**} & \multicolumn{2}{c}{**Mouse Speed Feature**} \\ \cline{2-7}  & **SSS** & **KSS** & **BR** & **PERCLOS** & **AMV** & **AOV** \\ \hline
1 & 0.357 & 0.268 & 0.040 & 0.099 & 0.041 & 0.059 \\ \hline
2 & 0.396 & 0.272 & 0.086 & 0.224 & 0.035 & 0.049 \\ \hline
3 & 0.889 & 0.805 & 0.095 & 0.255 & 0.030 & 0.041 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: TICC clustering results. Figure 4: Trend graph of each fatigue grade index of clustering results. Feature set 1: only eye-based features (18 features), the feature set includes BR and PERCLOS of each segment. Feature set 2: only mouse's velocity-based features (18 features). Feature set 3: only eye-based and mouse's velocity-based features (36 features). In order to obtain an appropriate time window to extract features, we evaluated the performance of fatigue detection at different time windows and different time durations, respectively. Tables 5 and 6 show the results of the runs in the 20 s-60 s sliding window and the 10 s-50 s sliding step. The range of sliding window and sliding step size is consistent in all experiments. The following will not be repeated. We can conclude that feature set 1 is under the condition of 60 s sliding window and 10 s sliding step growth. RF gets the best classification accuracy. The accuracy of classification is 83.57. In feature set 1, with different sliding windows, we can conclude that the KNN and RF algorithms are more accurate in fatigue detection as the sliding window size gets longer. There is some fluctuation in SVM detection, but the maximum SVM accuracy is reached when a window size of 60 s is reached. With different steps, KNN is influenced by the sliding step. The KNN fluctuates in accuracy as the sliding step size increases. The accuracy of SVM is highest in the shortest time window. Tables 7 and 8 show the results about feature set 2. The best accuracy is obtained by KNN in 60 s sliding window and 10 s sliding step. The best accuracy was 86.71. The accuracy of these three algorithms increases monotonically as the sliding window changes. The KNN algorithm performs best in time windows of 20 s and 60 s. SVM presents poorer performance in all time windows. RF accuracy is highest in the 30 s  
From Tables 9 and 10, the results show that KNN algorithm has the best accuracy in 60 s window and 10 s step size in feature set 3. The accuracy rate was 91.83. The change of RF accuracy in all windows is relatively stable. With different sliding windows, a monotonic increase in performance occurred for all three algorithms. Furthermore, we found that KNN worked best in the 40 s, 50 s and 60 s time windows. The accuracy of the RF has improved steadily overall. With different steps, all three algorithms show a decreasing trend in accuracy. From Tables 5-11, the above experimental results fully illustrate that the 60 s time window for extracting features is very effective for detecting fatigue. The model requires 60 s except for the first window, and then it only takes 10 s to detect fatigue in the future. In the comparison of the three feature sets, we can conclude that the highest accuracy is in feature set 3. We find that among all the sliding windows and sliding steps, the 60 s sliding step and the 10 s sliding step have the best performance.