In addition to several quantitative eye gaze measures previously discussed, eye gaze points representing small areas on the screen where the user is looking at during the period of eye gaze tracking experiment could also be analysed. Different eye tracking systems provide different methods and levels of support for eye gaze point estimates. For instance, GazeTracker provides the observer with GazeTrail, a diagram showing connected gaze points over a period of time superimposed over a stimulus, and LookZone, an area of interest (customisable by the observer) whose duration and order in which the user is looking at could be obtained (Eye Response Technologies, 2003). However, the accuracy of eye gaze point estimates is rarely validated. Several eye tracking systems leave it up to the observer to determine the accuracy, often by comparing the estimates to the points believed to be looked at by the user. This method is rather subjective and tedious, especially when a large number of participants are involved in the experiment and/or each experimental period lasts longer than a few minutes. Therefore, the validation of eye gaze point estimates could instead be integrated into the eye tracking system. This could be done by displaying stimuli on different parts of the screen (one by one) and having the user look at them serially, so that the system could determine distances between the stimuli and the estimated gaze points (called “displacement vectors”), and adapt itself accordingly to provide higher accuracy. The stimuli presented should be meaningful to the user, so that it is certain that the user would look at them. For example, numbers, letters, or figures could be used, and the user should be required to read them aloud, to show that they are really looking at these stimuli.
Displacement vectors are the indicator of the accuracy of eye gaze point estimates. Each vector has a direction that points from the estimated gaze point to the actual gaze point, and can be represented by a length/angle pair (e.g. 2cm, 120°) or an x-y coordinate (e.g. -1x+1.732y), as shown in Figure 5.6.
Figure 5.6 Example of displacement vector
There are two major factors contributing to the inaccuracy of gaze point estimates and hence the magnitude of the displacement vector: space and time. By space, it is meant that at a specific point in time, displacement vectors may or may not be the same for all positions (pixels) on the screen. And by time, it is meant that the displacement vector of a specific point on the screen may also change over time.
Therefore, in order to design a self-adaptive eye tracking system, these two factors should be taken into account for accuracy correction purposes. For example, at the beginning of the tracking, it may not be known whether the displacement vectors of different points on the screen will differ by how much or whether they will change over time. Thus, at first, a number of stimuli should be presented on as many parts of the screen as possible, and the presentation should be repeated periodically and often (e.g. every five minutes). When the tracking progresses, if the displacement
30 1.732cm
Estimated gaze point
1cm
Actual gaze point
vectors of different points on the screen do not differ by much, the number of stimuli in each presentation could be reduced. And if the displacement vector of a specific point on the screen does not change too often over time, the interval between each stimuli presentation could be made longer, therefore involving less interruption to the user.
If the eye tracking system automatically adjusts itself using periodical presentations of stimuli on the screen as previously discussed, it is expected that the effects of the influential factors on estimated eye gaze point accuracy could be kept at a minimum at all times during the period of experiment. However, at present, only a few (if any) eye tracking systems have the ability to do this, as most eye tracking systems available on the market allow calibration only at the beginning of the experiment. Furthermore, the calibration is usually done without verification for the accuracy. For example, the faceLAB eye tracking system displays stimuli (white dots) on the screen and captures user’s eye positions when the user looks at different parts of the screen and uses this information to estimate the user’s gaze points.
However, once the calibration has finished, the accuracy is not verified in anyway, as the only option available is to recalibrate once again if the estimated gaze spot does not correspond well to the actual gaze spot of the user (as judged by the observer). At any rate, it is promising that the next generation of eye tracking systems will be able to incorporate the ability to self-adjust their estimated gaze point accuracy within the near future.
5.6 Conclusion
This chapter has discussed the role of eye tracking in usability evaluation, specifically for the Web. Various eye tracking tools have been presented as well as examples of related Web usability evaluation projects. This chapter concluded with the discussion of procedures commonly undertaken to evaluate Web sites using the eye tracking technique. Although eye gaze tracking is gaining more popularity among usability experts, other usability evaluation techniques should not be totally disregarded. Instead, they could be used as a complement to the eye tracking technique, as appropriate.