## 6 Event Measures  
As stated in Section 4.3, classifying each gaze point as a specific event was just the first step in event detection. The next step was to merge the neighboring points with the same classification into one event, as shown in Figure 3. In this way, events with their own properties were created. For instance, fixations were characterized by a location (calculated as the mean or median of gaze points), as well as an onset and offset, while saccades and smooth pursuits were defined by their start and end points.  
The main problem with all threshold-based methods is their high susceptibility to outliers. When one gaze point in the middle of a fixation is classified as any other event, it automatically divides the fixation into two separate fixations. The popular solution is to merge subsequent fixations based on their low spatial and temporal distance. However, this introduces two more thresholds that must be manually assigned.  
Machine learning-based algorithms are more robust to this problem as they classify gaze points, taking into account not only the sample's properties but also its neighborhood. However, the problem still exists. To analyze this phenomenon for our results, we merged the neighboring event points to form events. Then, for each event type, we calculated two metrics, that is, the mean number of events and the mean event duration.  
The results in Table 6 show the number of events and mean duration of events for different combinations of features and ground truth (manual coding).  
Generally, our methods find more fixations than GT, which probably means that some actual fixations are classified as two or more separate fixations by the model. This is especially visible for AD and AJD combinations, where the number of detected fixations is  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Metrics** & **VAJD** & **VAD** & **VJD** & **AJD** & **VD** & **AD** & **GT** \\ \hline Mean No. of Fix & 12.5 & 13.5 & 14.5 & 48 & 18.3 & 40 & 10 \\ \hline Mean No of Sacc & 14.6 & 15 & 15 & 37 & 15.3 & 47.5 & 15.5 \\ \hline Mean No. of PSOs & 11.3 & 11.3 & 11.1 & 19.6 & 11 & 26.1 & 11 \\ \hline Mean No. of SP & 9.8 & 10.8 & 11.5 & 58.5 & 15.3 & 71 & 7 \\ \hline Mean FIX-dur & 217.2 & 209.1 & 203.1 & 49.3 & 158.2 & 23.9 & 226 \\ \hline Mean Sac-dur & 25.1 & 25.3 & 25.0 & 17.4 & 24.9 & 25.5 & 24 \\ \hline Mean PSO-dur & 16.8 & 16.3 & 16.8 & 11.0 & 17.0 & 11.7 & 16 \\ \hline Mean SP-dur & 403.8 & 366.2 & 365.2 & 60.5 & 280.3 & 73.0 & 508 \\ \hline \end{tabular}
\end{table}
Table 6: The event measures detected in the test data for different feature combinations by both manual annotation and the proposed model. The column event measure shows the list of event-measuring metrics, and the columns VAJD, VJD, VAD, AJD, VD, and AD are the results for feature sets.  
Figure 6: The confusion matrix for VAD and AJD feature combinations.  
about five times larger than it should be. A similar situation can be observed for smooth pursuits. Not surprisingly, when more separate events were detected, the mean durations of events were shorter.  
The results show that the calculations of event-level statistics, such as the number and duration of events, may reveal additional information about the quality of the model. For instance, the confusion matrix of the VD model in Figure 4b suggests that its performance is similar to (or even better than) the VAD, VJD, and VAJD models for smooth pursuit detection. However, event-measuring metrics in Table 6 show that using the VD model resulted in the creation of much smoother pursuit events than for the VAD, VJD, and VAJD models and a shorter duration of these events. So, it may be assumed that the smooth pursuit detection performance of the VD model is lower than that of the VAD, VJD, and VAJD models, even when the sample-level accuracy is better.  
As a conclusion from the results, the proposed models with feature combinations VAJD, VAD, and VJD perform approximately the same, outperforming AJD, VD, and AD feature combinations.