3 research outputs found
An Approach to Track Reading Progression Using Eye-Gaze Fixation Points
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
Tracking the Progression of Reading Through Eye-gaze Measurements
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
A Novel Slip-Kalman Filter to Track the Progression of Reading Through Eye-Gaze Measurements
In this paper, we propose an approach to track the progression of eye-gaze
while reading a block of text on computer screen. The proposed approach will
help to accurately quantify reading, e.g., identifying the lines of text that
were read/skipped and estimating the time spent on each line, based on
commercially available inexpensive eye-tracking devices. The proposed approach
is based on a novel Slip Kalman filter that is custom designed to track the
progression of reading. The performance of the proposed method is demonstrated
using 25 pages eye-tracking data collected using a commercial desk-mounted
eye-tracking device