research-article

Detection of smooth pursuits using eye movement shape features

Authors:

Mélodie Vidal,

Andreas Bulling,

Hans GellersenAuthors Info & Claims

ETRA '12: Proceedings of the Symposium on Eye Tracking Research and Applications

Pages 177 - 180

https://doi.org/10.1145/2168556.2168586

Published: 28 March 2012 Publication History

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Abstract

Smooth pursuit eye movements hold information about the health, activity and situation of people, but to date there has been no efficient method for their automated detection. In this work we present a method to tackle the problem, based on machine learning. At the core of our method is a novel set of shape features that capture the characteristic shape of smooth pursuit movements over time. The features individually represent incomplete information about smooth pursuits but are combined in a machine learning approach. In an evaluation with eye movements collected from 18 participants, we show that our method can detect smooth pursuit movements with an accuracy of up to 92%, depending on the size of the feature set used for their prediction. Our results have twofold significance. First, they demonstrate a method for smooth pursuit detection in mainstream eye tracking, and secondly they highlight the utility of machine learning for eye movement analysis.

References

[1]

Abd-almageed, W., Fadali, M. S., Bebis, G., Sami, M., and Bebis, F. G. 2002. A non-intrusive kalman filter-based tracker for pursuit eye movement. In In American Control Conference.

Google Scholar

[2]

Cecchin, T., Sauter, D., Brie, D., and Martin, B. 1990. On-line separation of smooth pursuit and saccadic eye movements. In Proc. of the 12th International Conference of the IEEE Engineering in Medicine and Biology Society, 777--778.

Google Scholar

[3]

Grindinger, T. 2006. Eye Movement Analysis and Prediction with the Kalman Filter. Master's thesis, Clemson University, USA.

Google Scholar

[4]

Jain, A. K., Murty, M. N., and Flynn, P. J. 1999. Data clustering: a review. ACM Computing Surveys 31, 264--323.

Digital Library

Google Scholar

[5]

Koh, D. H., Munikrishne Gowda, S., and Komogortsev, O. 2010. Real time eye movement identification protocol. In Extended Abstracts of the 28th International Conference on Human Factors in Computing Systems, 3499--3504.

Digital Library

Google Scholar

[6]

Kohonen, T., and Somervuo, P. 1998. Self-organizing maps of symbol strings. Neurocomputing 21, 1--3, 19--30.

Crossref

Google Scholar

[7]

Komogortsev, O. V., and Khan, J. I. 2007. Kalman filtering in the design of eye-gaze-guided. System, 679--689.

Google Scholar

[8]

Larsson, L. 2010. Event detection in eye-tracking data. Master's thesis, Lund University.

Google Scholar

[9]

Liang, Y., and Lee, J. D. 2008. Driver cognitive distraction detection using eye movements. In Passive Eye Monitoring, R. I. Hammoud, Ed., Signals and Communication Technology. Springer Berlin Heidelberg, 285--300.

Google Scholar

[10]

Miller, C. J. 2008. Real-Time Feature Extraction and Classification of Prehensile EMG Signals. Master's thesis, San Diego State University, USA.

Google Scholar

[11]

Robinson, D. 1965. The mechanics of human smooth pursuit eye movement. The Journal of Physiology 180, 3 (Oct.), 569--591.

Crossref

Google Scholar

[12]

San Agustin Lopez, J. 2009. Off-the-shelf Gaze Interaction. PhD thesis, IT University of Copenhagen.

Google Scholar

[13]

Vidal, M., Bulling, A., and Gellersen, H. 2011. Analysing eog signal features for the discrimination of eye movements with wearable devices. In Proc. of the 1st International Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction, 15--20.

Digital Library

Google Scholar

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https://dl.acm.org/doi/10.1145/3655601
Nurlatifa HHartanto RAtaka AWibirama S(2024)Optimum Object Selection Methods for Spontaneous Gaze-Based Interaction with Linear and Circular TrajectoriesResults in Engineering10.1016/j.rineng.2024.101769(101769)Online publication date: Jan-2024
https://doi.org/10.1016/j.rineng.2024.101769
Delamare WHarada DYang LRen X(2024)Guiding gaze gestures on smartwatchesInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103196183:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.ijhcs.2023.103196
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Index Terms

Detection of smooth pursuits using eye movement shape features
1. Computing methodologies
  1. Machine learning
2. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

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Information

Published In

ETRA '12: Proceedings of the Symposium on Eye Tracking Research and Applications

March 2012

420 pages

ISBN:9781450312219

DOI:10.1145/2168556

Conference Chairs:
Carlos H. Morimoto
University of São Paulo, Brazil
,
Howell Istance
Montfort University, UK
,
Editor:
Stephen N. Spencer
University of Washington
,
Program Chairs:
Jeffrey B. Mulligan
NASA
,
Pernilla Qvarfordt
FX Palo Alto

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 March 2012

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ETRA '12

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ETRA '12: Eye Tracking Research and Applications

March 28 - 30, 2012

California, Santa Barbara

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Overall Acceptance Rate 69 of 137 submissions, 50%

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Hou BNewn JSidenmark LKhan AGellersen H(2024)GazeSwitch: Automatic Eye-Head Mode Switching for Optimised Hands-Free PointingProceedings of the ACM on Human-Computer Interaction10.1145/36556018:ETRA(1-20)Online publication date: 28-May-2024
https://dl.acm.org/doi/10.1145/3655601
Nurlatifa HHartanto RAtaka AWibirama S(2024)Optimum Object Selection Methods for Spontaneous Gaze-Based Interaction with Linear and Circular TrajectoriesResults in Engineering10.1016/j.rineng.2024.101769(101769)Online publication date: Jan-2024
https://doi.org/10.1016/j.rineng.2024.101769
Delamare WHarada DYang LRen X(2024)Guiding gaze gestures on smartwatchesInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103196183:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.ijhcs.2023.103196
Castilla DDel Tejo Catalá OPons PSignol FRey BSuso-Ribera CPerez-Cortes J(2024)Improving the understanding of web user behaviors through machine learning analysis of eye-tracking dataUser Modeling and User-Adapted Interaction10.1007/s11257-023-09373-y34:2(293-322)Online publication date: 1-Apr-2024
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