An Approach to Track Reading Progression Using Eye-Gaze Fixation Points

TL;DR

This paper presents a novel method using hidden Markov models to improve the accuracy of tracking reading lines via eye-gaze data from commercial trackers, enabling advanced reading analysis applications.

Contribution

It introduces statistical models and hidden Markov models to enhance line detection accuracy from noisy eye-gaze data during reading.

Findings

Over 20% improvement in line detection accuracy

Effective handling of noisy eye-gaze data

Potential for applications in comprehension and interest detection

Abstract

In this paper, we consider the problem of tracking the eye-gaze of individuals while they engage in reading. Particularly, we develop ways to accurately track the line being read by an individual using commercially available eye tracking devices. Such an approach will enable futuristic functionalities such as comprehension evaluation, interest level detection, and user-assisting applications like hands-free navigation and automatic scrolling. Existing commercial eye trackers provide an estimated location of the eye-gaze fixations every few milliseconds. However, this estimated data is found to be very noisy. As such, commercial eye-trackers are unable to accurately track lines while reading. In this paper we propose several statistical models to bridge the commercial gaze tracker outputs and eye-gaze patterns while reading. We then employ hidden Markov models to parametrize these statistical models and to accurately detect the line being read. The proposed approach is shown to yield an improvement of over 20% in line detection accuracy.