## 5 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**F1 Score**} & \\ \cline{2-6}
**Algorithms** & **Fixations** & **Saccades** & **PSOs** & **SPs** & **Mean F1** & **Mean K** \\ \hline VAJD & 0.792 & 0.918 & 0.815 & 0.80 & 0.83 & 0.733 \\ \hline VAD & 0.803 & 0.914 & 0.818 & 0.803 & 0.834 & 0.735 \\ \hline VJD & 0.798 & 0.908 & 0.795 & 0.805 & 0.826 & 0.723 \\ \hline VD & 0.772 & 0.914 & 0.797 & 0.794 & 0.819 & 0.708 \\ \hline
1D-CNN-BLSTM [21]* & 0.667 & 0.72 & - & 0.663 & 0.7 & 0.50 \\ \hline
1D-CNN-BLSTM(VD) ** & 0.52 & 0.79 & 0.61 & 0.61 & 0.63 & 0.48 \\ \hline AJD & 0.644 & 0.679 & 0.468 & 0.641 & 0.608 & 0.526 \\ \hline AD & 0.37 & 0.543 & 0.31 & 0.464 & 0.42 & 0.235 \\ \hline \hline \end{tabular}
* The 1D-CNN-BLSTM row shows the results reported in [21], classifying the three event types—fixation, saccade, and SP—using the VD feature set on the dataset used in this paper. ** The 1D-CNN-BLSTM (VD) refers to our changed and re-implemented version of their model (classifying four events).  
\end{table} 
Table 2: Comparison of F1 score (F1) for each event type, mean F1 score, and mean Cohen’s kappa (K). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**Precision Score**} & \\ \cline{2-5}
**Algorithms** & **Fixations** & **Saccades** & **PSOs** & **SPs** & **Mean Precision** \\ \hline VAJD & 0.766 & 0.937 & 0.935 & 0.733 & 0.842 \\ \hline VAD & 0.788 & 0.919 & 0.935 & 0.739 & 0.845 \\ \hline VJD & 0.778 & 0.917 & 0.921 & 0.735 & 0.837 \\ \hline VD & 0.7642 & 0.919 & 0.932 & 0.717 & 0.833 \\ \hline
1D-CNN-BLSTM(VD) & 0.64 & 0.78 & 0.79 & 0.56 & 0.69 \\ \hline AJD & 0.607 & 0.642 & 0.804 & 0.559 & 0.765 \\ \hline AD & 0.517 & 0.458 & 0.617 & 0.371 & 0.490 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Precision of each event type and feature combination. We compare the performance of the proposed model with different feature combinations. both are low-speed movement types. Therefore, the direction is used to detect fixations from SPs. A classic VD set performs better than the former sets for SPs, but its performance is still low for PSOs. Adding acceleration and jerk improves the general performance of the models, especially for PSO detection. A comparison of VAD, VJD, and VAJD sets shows that both acceleration and jerk have a similar impact on the results, and their combination is only marginally better than using only one of them (see Tables 4 and 5). A comparison with the results of the 1D-CNN-BLSTM model on the same dataset, as reported in [21], shows that our model performs better and can also distinguish the PSO events. Our modified implementation of the same model does not perform well. However, one of the reasons may be that we changed the model to return only one value instead of the sequence, and skipped the model optimization step. confused (45% of fixations are classified as smooth pursuits). Moreover, the recall of PSOs is very low, and most of the actual PSOs are misclassified (only 21% are classified correctly). The velocity and direction (VD) combination shows a noticeable improvement over the AD combination. The combination significantly improves the performance of fixations, saccades, and smooth pursuits. However, its performance in correctly identifying PSOs from fixations and smooth pursuits is lower. So, we propose using the VAJD, VAD, AJD, and VJD combinations to improve PSO identification performance and reduce misclassifications between PSOs and other event types. Combining velocity and direction with acceleration and/or jerk improved the overall identification performance for all event types compared to other feature combinations, especially for PSO detection. However, the performance of PSO identification is still low, and many PSO points are misclassified as fixations and smooth pursuits. The results for these feature combinations are shown in Figure 5. For example, the results of the VAJD feature combination in Figure 5b show that 11% of PSOs are misclassified as smooth pursuits and 11% as fixations.