EyeO: Autocalibrating Gaze Output with Gaze Input for Gaze Typing
Abstract
Gaze tracking devices have the potential to expand interactivity greatly, yet miscalibration remains a significant barrier to use. As devices miscalibrate, people tend to compensate by intentionally offsetting their gaze, which makes detecting miscalibration from eye signals difficult. To help address this problem, we propose a novel approach to seamless calibration based on the insight that the system's model of eye gaze can be updated during reading (user does not compensate) to improve calibration for typing (user might compensate). To explore this approach, we built an auto-calibrating gaze typing prototype called EyeO and ran a user study with 20 participants. Our user study results suggest that seamless autocalibration can significantly improve typing efficiency and user experience.