Eye Tracking Research & Application 

Abstract: An eye tracking study was conducted to evaluate specific design features for a prototype web portal application. This software serves independent web content through separate, rectangular, user-modifiable portlets on a web page. Each of seven participants navigated across multiple web pages while conducting six specific tasks, such as removing a link from a portlet. Specific experimental questions included (1) whether eye tracking-derived parameters were related to page sequence or user actions preceding page visits, (2) whether users were biased to traveling vertically or horizontally while viewing a web page, and (3) whether specific sub-features of portlets were visited in any particular order. Participants required 2-15 screens, and from 7-360+ seconds to complete each task. Based on analysis of screen sequences, there was little evidence that search became more directed as screen sequence increased. Navigation among portlets, when at least two columns exist, was biased towards horizontal search (across columns) as opposed to vertical search (within column). Within a portlet, the header bar was not reliably visited prior to the portlet's body, evidence that header bars are not reliably used for navigation cues. Initial design recommendations emphasized the need to place critical portlets on the left and top of the web portal area, and that related portlets do not need to appear in the same column. Further experimental replications are recommended to generalize these results to other applications.

Abstract: We report on the results of a study in which pairs of subjects were involved in spoken dialogues and one of the subjects also operated a simulated vehicle. We estimated the driver's cognitive load based on pupil size measurements from a remote eye tracker. We compared the cognitive load estimates based on the physiological pupillometric data and driving performance data. The physiological and performance measures show high correspondence suggesting that remote eye tracking might provide reliable driver cognitive load estimation, especially in simulators. We also introduced a new pupillometric cognitive load measure that shows promise in tracking cognitive load changes on time scales of several seconds.

Abstract: The lack of a common benchmark for the evaluation of the gaze estimation task from RGB and RGB-D data is a serious limitation for distinguishing the advantages and disadvantages of the many proposed algorithms found in the literature. This paper intends to overcome this limitation by introducing a novel database along with a common framework for the training and evaluation of gaze estimation approaches. In particular, we have designed this database to enable the evaluation of the robustness of algorithms with respect to the main challenges associated to this task: i) Head pose variations; ii) Person variation; iii) Changes in ambient and sensing conditions and iv) Types of target: screen or 3D object.

Abstract: Data quality is essential to the validity of research results and to the quality of gaze interaction. We argue that the lack of standard measures for eye data quality makes several aspects of manufacturing and using eye trackers, as well as researching eye movements and vision, more difficult than necessary. Uncertainty regarding the comparability of research results is a considerable impediment to progress in the field. In this paper, we illustrate why data quality matters and review previous work on how eye data quality has been measured and reported. The goal is to achieve a common understanding of what data quality is and how it can be defined, measured, evaluated, and reported.