a robust algorithm for reading detection
As video cameras become cheaper and more pervasive, there is now increased opportunity for user interfaces to take advantage of user gaze data. Eye movements provide a powerful source of information that can be used to determine user intentions and interests. In this paper, we develop and test a method for recognizing when users are reading text based solely on eye-movement data. The experimental results show that our reading detection method is robust to noise, individual differences, and variations in text difficulty. Compared to a simple detection algorithm, our algorithm reliably, quickly, and accurately recognizes and tracks reading. Thus, we provide a means to capture normal user activity, enabling interfaces that incorporate more natural interactions of human and computer.
robust algorithm reading detection read detection video camera pervasive user user user interface interfaces gaze data informal user intent paper test method text eye-movement experimentation noise individual differences variation tracking normalization activation natural interaction human computation attentive systems gaze tracking gaze-based interfaces user interest tracking users