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Poster: A Preliminary Investigation on Eye Gaze-based Concentration Recognition during Silent Reading of Text

Published: 08 June 2022 Publication History

Abstract

We propose machine learning models to recognize state of non-concentration using eye-gaze data to increase the productivity. The experimental results show that Random Forest classifier with a 12 s window can divide the states with an F1-score more than 0.9.

References

[1]
Myrthe Faber, Robert Bixler, and Sidney K D’Mello. 2018. An automated behavioral measure of mind wandering during computerized reading. Behavior Research Methods 50, 1 (2018), 134–150.
[2]
Ministry of Internal Affairs and Communications. 2017. Work Style Reforms and ICT Utilization. (2017). https://www.soumu.go.jp/johotsusintokei/whitepaper/ja/h29/pdf/n4200000.pdfin Japanese.
[3]
School Partners. 2022. High School Newspaper Online. Retrieved Jan 25, 2022 from https://www.koukouseishinbun.jp/ in Japanese.
[4]
The Pennsylvania State University 2014. Psych 256: Cognitive Psychology SU14. The Pennsylvania State University. https://sites.psu.edu/psych256su14/2014/07/14/cognitive-resources/.
[5]
Kosuke Uchiyama, kazune Miyagi, Hirotake Ishii, Hiroshi Shimoda, Fumiaki obayashi, and Mikio Iwakawa. 2013. Development of an Evaluation Tool for Intellectual Productivity Based on Work Concentration. (2013). in Japanese.
[6]
Yuji Uema and Kazutaka Inoue. 2017. JINS MEME algorithm for estimation and tracking of concentration of users. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. 297–300.

Cited By

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  • (2024)A comprehensive assessment of deep learning techniques for eye gaze estimation: A comparative performance analysisInternational Journal of ADVANCED AND APPLIED SCIENCES10.21833/ijaas.2024.07.01211:7(101-110)Online publication date: Jul-2024
  • (2024)Self-efficacy Measurement Method Using Regression Models with Anticipatory Gaze for Supporting RehabilitationComputers Helping People with Special Needs10.1007/978-3-031-62849-8_38(311-319)Online publication date: 5-Jul-2024

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Published In

cover image ACM Conferences
ETRA '22: 2022 Symposium on Eye Tracking Research and Applications
June 2022
408 pages
ISBN:9781450392525
DOI:10.1145/3517031
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 June 2022

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Author Tags

  1. Cognitive resource
  2. Concentration level
  3. Eye gaze
  4. Machine learning
  5. Reading

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  • Abstract
  • Research
  • Refereed limited

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ETRA '22

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ETRA '22 Paper Acceptance Rate 15 of 39 submissions, 38%;
Overall Acceptance Rate 69 of 137 submissions, 50%

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ETRA '25

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Cited By

View all
  • (2024)A comprehensive assessment of deep learning techniques for eye gaze estimation: A comparative performance analysisInternational Journal of ADVANCED AND APPLIED SCIENCES10.21833/ijaas.2024.07.01211:7(101-110)Online publication date: Jul-2024
  • (2024)Self-efficacy Measurement Method Using Regression Models with Anticipatory Gaze for Supporting RehabilitationComputers Helping People with Special Needs10.1007/978-3-031-62849-8_38(311-319)Online publication date: 5-Jul-2024

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