Eye-trackers can tell us where people look on a computer screen. The technology used to be prohibitively expensive to be integrated into consumer devices and its use was thus limited to research studies. The last few years however brought us a new generation of eye-trackers that are both accurate and sufficiently affordable to be integrated into regular workstations (search Tobii EyeX). This paves the way for an entirely new breed of interactive systems which track a users’ gaze to learn their behavior, preferences, and interests, then adapt intelligently in real time to support the user’s needs.
This project aims to lay the foundation for creating gaze adaptive systems in the area of visual data analytics.
Explore ways in which we can use raw eye-tracking data (i.e., where a user looks) to infer a higher level understanding of a user’s goals, tasks, preferences, etc.
Design/implement gaze-adaptive interactive responses in established data visualizations and visual data-analytic systems.
Test the effectiveness of such designs via user studies
• Human factors (e.g., vision, cognition, task analysis)
• User studies
• Iterative software design and development, rapid prototyping