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

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Title: Tracking the Progression of Reading Using Eye-Gaze Point Measurements and Hidden Markov Models.
Authors: Bottos, Stephen1 bottos@uwindsor.ca, Balasingam, Balakumar1 singam@uwindsor.ca
Source: IEEE Transactions on Instrumentation & Measurement. Oct2020, Vol. 69 Issue 10, p7857-7868. 12p.
Subjects: Markov processes, Kalman filtering, Gaze, Eye tracking, Statistical models, Web designers
Abstract: In this article, we consider the problem of tracking the eye gaze of individuals while they engage in reading. In particular, we develop the 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 such as hand-free navigation and automatic scrolling. Furthermore, the proposed approach will pave the way to develop technology that may generate valuable feedback to content makers, such as web designers, authors, educators, and social media users. The existing commercial eye trackers provide an estimated location of the eye-gaze points every few milliseconds. However, these estimated gaze points are not sufficient to quantify reading progression—a specific eye-gaze activity. In this article, we propose algorithms to bridge the commercial gaze tracker outputs and informative eye-gaze patterns while reading. The proposed system consists of Kalman filters and hidden Markov models to parameterize these statistical models and to accurately detect the line being read. The proposed approach is shown to yield an improvement of 27.1% in line detection accuracy over line tracking using estimated eye-gaze points alone by the eye tracker. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Instrumentation & Measurement is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: Tracking the Progression of Reading Using Eye-Gaze Point Measurements and Hidden Markov Models.
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  Data: <searchLink fieldCode="AR" term="%22Bottos%2C+Stephen%22">Bottos, Stephen</searchLink><relatesTo>1</relatesTo><i> bottos@uwindsor.ca</i><br /><searchLink fieldCode="AR" term="%22Balasingam%2C+Balakumar%22">Balasingam, Balakumar</searchLink><relatesTo>1</relatesTo><i> singam@uwindsor.ca</i>
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  Data: <searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Gaze%22">Gaze</searchLink><br /><searchLink fieldCode="DE" term="%22Eye+tracking%22">Eye tracking</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Web+designers%22">Web designers</searchLink>
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  Data: In this article, we consider the problem of tracking the eye gaze of individuals while they engage in reading. In particular, we develop the 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 such as hand-free navigation and automatic scrolling. Furthermore, the proposed approach will pave the way to develop technology that may generate valuable feedback to content makers, such as web designers, authors, educators, and social media users. The existing commercial eye trackers provide an estimated location of the eye-gaze points every few milliseconds. However, these estimated gaze points are not sufficient to quantify reading progression—a specific eye-gaze activity. In this article, we propose algorithms to bridge the commercial gaze tracker outputs and informative eye-gaze patterns while reading. The proposed system consists of Kalman filters and hidden Markov models to parameterize these statistical models and to accurately detect the line being read. The proposed approach is shown to yield an improvement of 27.1% in line detection accuracy over line tracking using estimated eye-gaze points alone by the eye tracker. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Instrumentation & Measurement is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TIM.2020.2983525
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        Text: English
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        Type: general
      – SubjectFull: Kalman filtering
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      – SubjectFull: Gaze
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      – SubjectFull: Statistical models
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      – SubjectFull: Web designers
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              Text: Oct2020
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