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Detection of smooth pursuits using eye movement shape features

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published

Standard

Detection of smooth pursuits using eye movement shape features. / Vidal, Mélodie; Bulling, Andreas; Gellersen, Hans.
Proceedings of the Symposium on Eye Tracking Research and Applications. New York, NY, USA: ACM, 2012. p. 177-180 (ETRA '12).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Vidal, M, Bulling, A & Gellersen, H 2012, Detection of smooth pursuits using eye movement shape features. in Proceedings of the Symposium on Eye Tracking Research and Applications. ETRA '12, ACM, New York, NY, USA, pp. 177-180. https://doi.org/10.1145/2168556.2168586

APA

Vidal, M., Bulling, A., & Gellersen, H. (2012). Detection of smooth pursuits using eye movement shape features. In Proceedings of the Symposium on Eye Tracking Research and Applications (pp. 177-180). (ETRA '12). ACM. https://doi.org/10.1145/2168556.2168586

Vancouver

Vidal M, Bulling A, Gellersen H. Detection of smooth pursuits using eye movement shape features. In Proceedings of the Symposium on Eye Tracking Research and Applications. New York, NY, USA: ACM. 2012. p. 177-180. (ETRA '12). doi: 10.1145/2168556.2168586

Author

Vidal, Mélodie ; Bulling, Andreas ; Gellersen, Hans. / Detection of smooth pursuits using eye movement shape features. Proceedings of the Symposium on Eye Tracking Research and Applications. New York, NY, USA : ACM, 2012. pp. 177-180 (ETRA '12).

Bibtex

@inproceedings{bb7540d114c64ce9bd3d8e10ef6b074e,
title = "Detection of smooth pursuits using eye movement shape features",
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.",
author = "M{\'e}lodie Vidal and Andreas Bulling and Hans Gellersen",
year = "2012",
doi = "10.1145/2168556.2168586",
language = "English",
isbn = "978-1-4503-1221-9",
series = "ETRA '12",
publisher = "ACM",
pages = "177--180",
booktitle = "Proceedings of the Symposium on Eye Tracking Research and Applications",

}

RIS

TY - GEN

T1 - Detection of smooth pursuits using eye movement shape features

AU - Vidal, Mélodie

AU - Bulling, Andreas

AU - Gellersen, Hans

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

U2 - 10.1145/2168556.2168586

DO - 10.1145/2168556.2168586

M3 - Conference contribution/Paper

SN - 978-1-4503-1221-9

T3 - ETRA '12

SP - 177

EP - 180

BT - Proceedings of the Symposium on Eye Tracking Research and Applications

PB - ACM

CY - New York, NY, USA

ER -