Tracking the Progression of Reading Through Eye-gaze Measurements

TL;DR

This paper introduces a novel eye-gaze tracking algorithm that accurately monitors reading progression along text lines, aiding in assessing attention and reading behavior with potential educational and usability applications.

Contribution

The paper extends previous fixation analysis to include tracking reading progression along lines using least squares estimation of eye movement states.

Findings

Successfully tracked reading progression in simulated data

Demonstrated effectiveness on real eye-tracking data

Potential for assessing reading attention and engagement

Abstract

In this paper we consider the problem of tracking the progression of reading through eye-gaze measurements. Such an algorithm is novel and will ultimately help to develop a method of analyzing eye-gaze data which had been collected during reading activity in order to uncover crucial information regarding the individual's interest level and quality of experience while reading a passage of text or book. Additionally, such an approach will serve as a "visual signature" - a method of verifying if an individual has indeed given adequate attention to critical text-based information. Further, an accurate "reading-progression-tracker" has potential applications in educational institutions, e-readers and parenting solutions. Tracking the progression of reading remains a challenging problem due to the fact that eye-gaze movements are highly noisy and the eye-gaze is easily distracted in a limited space, like an e-book. In a prior work, we proposed an approach to analyze eye-gaze fixation points collected while reading a page of text in order to classify each measurement to a line of text; this approach did not consider tracking the progression of reading along the line of text. In this paper, we extend the capabilities of the previous algorithm in order to accurately track the progression of reading along each line. the proposed approach employs least squares batch estimation in order to estimate three states of the horizontal saccade: position, velocity and acceleration. First, the proposed approach is objectively evaluated on a simulated eye-gaze dataset. Then, the proposed algorithm is demonstrated on real data collected by a Gazepoint eye-tracker while the subject is reading several pages from an electronic book.