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User Eye Fatigue Detection via Eye Movement Behavior

Authors:

Evgeniy Abdulin,

Oleg KomogortsevAuthors Info & Claims

CHI EA '15: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems

Pages 1265 - 1270

https://doi.org/10.1145/2702613.2732812

Published: 18 April 2015 Publication History

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Abstract

In this study we propose and evaluate a novel approach that allows detection of physical eye fatigue. The proposed approach is based on the analysis of the recorded eye movements via what is called behavioral scores. These easy-to-compute scores can be obtained immediately after a calibration procedure, via processing of such basic eye movements as fixations and saccades extracted from the raw eye positional data recorded by an eye tracker. The results, based on the data from 36 volunteers indicate that one of the behavioral scores, Fixational Qualitative Score, is more sensitive to the onset of eye fatigue than already established methods based on saccadic characteristics only.

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Urtado MRodrigues RFukusima S(2024)Visual Field Restriction in the Recognition of Basic Facial Expressions: A Combined Eye Tracking and Gaze Contingency StudyBehavioral Sciences10.3390/bs1405035514:5(355)Online publication date: 23-Apr-2024
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Le Tarnec HAugereau OBevacqua EDe Loor P(2024)Impact of Augmented Engagement Model for Collaborative Avatars on a Collaborative Task in Virtual RealityProceedings of the 2024 International Conference on Advanced Visual Interfaces10.1145/3656650.3656684(1-9)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3656650.3656684
Kui XZhang Mliu QWang ZHuang GZheng ZXia JZhang C(2024)TSSeer: a visual analytics approach for exploring the correlation between teachers’ multimodal emotions and students’ behaviors in massive open online coursesJournal of Visualization10.1007/s12650-024-00988-w27:4(749-764)Online publication date: 22-Apr-2024
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Index Terms

User Eye Fatigue Detection via Eye Movement Behavior
1. Human-centered computing
  1. Human computer interaction (HCI)

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Information & Contributors

Information

Published In

CHI EA '15: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems

April 2015

2546 pages

ISBN:9781450331463

DOI:10.1145/2702613

General Chairs:
Bo Begole
Huawei, USA
,
Jinwoo Kim
Yonsei University, Korea
,
Program Chairs:
Kori Inkpen
Microsoft Research, USA
,
Woontack Woo
KAIST, Korea

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: 18 April 2015

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Work in progress

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Conference

CHI '15

Sponsor:

SIGCHI

CHI '15: CHI Conference on Human Factors in Computing Systems

April 18 - 23, 2015

Seoul, Republic of Korea

Acceptance Rates

CHI EA '15 Paper Acceptance Rate 379 of 1,520 submissions, 25%;

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

Upcoming Conference

CHI '25

Sponsor:
sigchi

CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

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Cited By

View all

Urtado MRodrigues RFukusima S(2024)Visual Field Restriction in the Recognition of Basic Facial Expressions: A Combined Eye Tracking and Gaze Contingency StudyBehavioral Sciences10.3390/bs1405035514:5(355)Online publication date: 23-Apr-2024
https://doi.org/10.3390/bs14050355
Le Tarnec HAugereau OBevacqua EDe Loor P(2024)Impact of Augmented Engagement Model for Collaborative Avatars on a Collaborative Task in Virtual RealityProceedings of the 2024 International Conference on Advanced Visual Interfaces10.1145/3656650.3656684(1-9)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3656650.3656684
Kui XZhang Mliu QWang ZHuang GZheng ZXia JZhang C(2024)TSSeer: a visual analytics approach for exploring the correlation between teachers’ multimodal emotions and students’ behaviors in massive open online coursesJournal of Visualization10.1007/s12650-024-00988-w27:4(749-764)Online publication date: 22-Apr-2024
https://doi.org/10.1007/s12650-024-00988-w
Hayakawa YFujinami KYamamoto JTsuji A(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
https://doi.org/10.1007/978-3-031-62849-8_38
Lengenfelder CHild JVoit MPeinsipp-Byma E(2023)Pilot Study on Gaze-Based Mental Fatigue Detection During Interactive Image ExploitationEngineering Psychology and Cognitive Ergonomics10.1007/978-3-031-35392-5_8(109-119)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-35392-5_8
Ray ASahana SDas SDas I(2023)Cursor Motion Control Using Eye Tracking and Computer VisionAdvanced Communication and Intelligent Systems10.1007/978-3-031-25088-0_62(706-714)Online publication date: 15-Feb-2023
https://doi.org/10.1007/978-3-031-25088-0_62
Zhou CYuan FHuang TZhang YKaner J(2022)The Impact of Interface Design Element Features on Task Performance in Older Adults: Evidence from Eye-Tracking and EEG SignalsInternational Journal of Environmental Research and Public Health10.3390/ijerph1915925119:15(9251)Online publication date: 28-Jul-2022
https://doi.org/10.3390/ijerph19159251
Wang YZhang LFang Z(2022)Eye Fatigue Detection through Machine Learning Based on Single Channel ElectrooculographyAlgorithms10.3390/a1503008415:3(84)Online publication date: 3-Mar-2022
https://doi.org/10.3390/a15030084
Russell BMcDaid AToscano WHume P(2021)Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning ModelSensors10.3390/s2116544221:16(5442)Online publication date: 12-Aug-2021
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Grzelak JBrandao MFourcade MKuipers BLazar SMulligan D(2021)The Dangers of Drowsiness Detection: Differential Performance, Downstream Impact, and MisusesProceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3461702.3462593(525-531)Online publication date: 21-Jul-2021
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