research-article

LSTMs can distinguish dental expert saccade behavior with high ”plaque-urracy”

Authors:

Jonas Frankemölle,

Constanze Keutel,

Fabian Huettig,

Enkelejda KasneciAuthors Info & Claims

ETRA '22: 2022 Symposium on Eye Tracking Research and Applications

Article No.: 8, Pages 1 - 7

https://doi.org/10.1145/3517031.3529631

Published: 08 June 2022 Publication History

Abstract

Much of the current expertise literature has found that domain specific tasks evoke different eye movements. However, research has yet to predict optimal image exploration using saccadic information and to identify and quantify differences in the search strategies between learners, intermediates, and expert practitioners. By employing LSTMs for scanpath classification, we found saccade features over time could distinguish all groups at high accuracy. The most distinguishing features were saccade velocity peak (72%), length (70%), and velocity average (68%). These findings promote the holistic theory of expert visual exploration that experts can quickly process the whole scene using longer and more rapid saccade behavior initially. The potential to integrate expertise model development from saccadic scanpath features into intelligent tutoring systems is the ultimate inspiration for our research. Additionally, this model is not confined to visual exploration in dental xrays, rather it can extend to other medical domains.

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cover image ACM Conferences

ETRA '22: 2022 Symposium on Eye Tracking Research and Applications

June 2022

408 pages

ISBN:9781450392525

DOI:10.1145/3517031

Editors:
Frederick Shic
University of Washington, USA
,
Enkelejda Kasneci
University of Tübingen, Germany
,
Mohamed Khamis
University of Glasgow, Scotland
,
Hans Gellersen
Aarhus University, Denmark
,
Enkelejda Kasneci
University of Tübingen, Germany
,
Krzysztof Krejtz
SWPS University of Social Sciences and Humanities, Poland
,
Daniel Weiskopf
University of Stuttgart, Germany
,
Tanja Blascheck
Stuttgart University, Germany
,
Jessica Bradshaw
University of South Carolina, USA
,
Hana Vrzakova
University of Eastern Finland, Finland
,
Kamran Binaee
University of Nevada, USA
,
Michael Burch
University of Applied Sciences in Chur, Switzerland
,
Peter Kiefer
ETH Zurich, Switzerland
,
Roman Bednarik
University of Eastern Finland
,
Diako Mardanbegi
AdHawk Microsystems, Canada
,
Christopher Clarke
University of Bath, Chur, England
,
Rakshit Kothari
Rochester Institute of Technology, USA
,
Vijay Rajanna
Senior Research Engineer at Sensel, USA
,
Sampath Jayarathna
Old Dominion University, USA
,
Arantxa Villanueva
Public University of Navarra, Spain
,
Adham Atyabi
University of Colorado, USA
,
Shahram Eivazi
University of Tübingen, Germany

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https://doi.org/10.3389/frai.2024.1391745
Bozkir ERiedmiller CSkodras AKasneci GKasneci E(2024)Can You Tell Real from Fake Face Images? Perception of Computer-Generated Faces by HumansACM Transactions on Applied Perception10.1145/369666722:2(1-23)Online publication date: 30-Nov-2024
https://dl.acm.org/doi/10.1145/3696667
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