Maximizing reading: pattern analysis to describe points of gaze

Paeglis, Roberts; Bagucka, Kristine; Sjakste, Nikolajs; Lacis, Ivars

doi:10.1117/12.687470

Cited by 3 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous research outlines the complex nature of the simple task of reading, such as unpredictable re-visitation of previous passages and the skipping of words [6], [28]. A similar attempt to track reading progression [22] required the use of sophisticated equipment highly regulated conditions, and filtered data, focusing on the measurement of saccades and lingering gaze points. The hidden Markov modelbased algorithm aims to reduce dependency on external conditions, and offer more robust and accurate tracking given noisy data.…”

Section: Introductionmentioning

confidence: 99%

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

Bottos,

Balasingam

2019

Preprint

View full text Add to dashboard Cite

In this paper, we consider the problem of tracking the eyegaze 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 eyetrackers 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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Bottos,

Balasingam

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The study of reading itself however has continued to prove quite difficult in practice due to the complex nature of such a simple action [6], [14]. Recent works have addressed the issue of translating an individual's eye-gaze fixation points to an accurate interpretation of their reading patterns and habits with modern solutions [7], [11], [13], yielding results far more quickly and with unprecidented accuracy than ever before. There is one caveat however, while these modern solutions yield excellent results, they are still reliant on expensive hardware and regulated environments which restrict lighting, head movement, and distance from the text being read much like their primitive counterparts.…”

Section: Introductionmentioning

confidence: 99%

A Novel Slip-Kalman Filter to Track the Progression of Reading Through Eye-Gaze Measurements

Bottos¹,

Balachandran²

2019

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

View full text Add to dashboard Cite

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 eyetracking 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 eyetracking data collected using a commercial desk-mounted eye-tracking device.

show abstract

Tracking the Progression of Reading Using Eye-Gaze Point Measurements and Hidden Markov Models

Bottos

Balachandran

2020

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

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.

show abstract

Maximizing reading: pattern analysis to describe points of gaze

Cited by 3 publications

References 3 publications

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

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

A Novel Slip-Kalman Filter to Track the Progression of Reading Through Eye-Gaze Measurements

Tracking the Progression of Reading Using Eye-Gaze Point Measurements and Hidden Markov Models

Contact Info

Product

Resources

About