research-article

Detecting Autism Based on Eye-Tracking Data from Web Searching Tasks

Authors:

Victoria Yaneva,

Yeliz Yesilada,

Ruslan MitkovAuthors Info & Claims

W4A '18: Proceedings of the 15th International Web for All Conference

Article No.: 16, Pages 1 - 10

https://doi.org/10.1145/3192714.3192819

Published: 23 April 2018 Publication History

Abstract

The ASD diagnosis requires a long, elaborate, and expensive procedure, which is subjective and is currently restricted to behavioural, historical, and parent-report information. In this paper, we present an alternative way for detecting the condition based on the atypical visual-attention patterns of people with autism. We collect gaze data from two different kinds of tasks related to processing of information from web pages: Browsing and Searching. The gaze data is then used to train a machine learning classifier whose aim is to distinguish between participants with autism and a control group of participants without autism. In addition, we explore the effects of the type of the task performed, different approaches to defining the areas of interest, gender, visual complexity of the web pages and whether or not an area of interest contained the correct answer to a searching task. Our best-performing classifier achieved 0.75 classification accuracy for a combination of selected web pages using all gaze features. These preliminary results show that the differences in the way people with autism process web content could be used for the future development of serious games for autism screening. The gaze data, R code, visual stimuli and task descriptions are made freely available for replication purposes.

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Cited By

Lencastre PLotfigolian MLind P(2024)Identifying Autism Gaze Patterns in Five-Second Data RecordsDiagnostics10.3390/diagnostics1410104714:10(1047)Online publication date: 18-May-2024
https://doi.org/10.3390/diagnostics14101047
Grootjen JWeingärtner HMayer S(2024)Uncovering and Addressing Blink-Related Challenges in Using Eye Tracking for Interactive SystemsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642086(1-23)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642086
Varghese EQaraqe MAl-Thani D(2024)Attention Level Evaluation in Children With Autism: Leveraging Head Pose and Gaze Parameters From Videos for Educational InterventionIEEE Transactions on Learning Technologies10.1109/TLT.2024.340970217(1777-1793)Online publication date: 5-Jun-2024
https://dl.acm.org/doi/10.1109/TLT.2024.3409702
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Index Terms

Detecting Autism Based on Eye-Tracking Data from Web Searching Tasks
1. Human-centered computing
  1. Accessibility
    1. Accessibility design and evaluation methods
    2. Empirical studies in accessibility
  2. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies
    2. Interaction paradigms
      1. Web-based interaction
2. Social and professional topics
  1. User characteristics
    1. People with disabilities

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W4A '18: Proceedings of the 15th International Web for All Conference

April 2018

223 pages

ISBN:9781450356510

DOI:10.1145/3192714

Copyright © 2018 ACM.

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Published: 23 April 2018

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W4A '18

W4A '18: The Internet of Accessible Things

April 23 - 25, 2018

Lyon, France

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W4A '18 Paper Acceptance Rate 25 of 43 submissions, 58%;

Overall Acceptance Rate 171 of 371 submissions, 46%

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Cited By

Lencastre PLotfigolian MLind P(2024)Identifying Autism Gaze Patterns in Five-Second Data RecordsDiagnostics10.3390/diagnostics1410104714:10(1047)Online publication date: 18-May-2024
https://doi.org/10.3390/diagnostics14101047
Grootjen JWeingärtner HMayer S(2024)Uncovering and Addressing Blink-Related Challenges in Using Eye Tracking for Interactive SystemsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642086(1-23)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642086
Varghese EQaraqe MAl-Thani D(2024)Attention Level Evaluation in Children With Autism: Leveraging Head Pose and Gaze Parameters From Videos for Educational InterventionIEEE Transactions on Learning Technologies10.1109/TLT.2024.340970217(1777-1793)Online publication date: 5-Jun-2024
https://dl.acm.org/doi/10.1109/TLT.2024.3409702
Priya SS PKamisetty S(2024)Enhanced Detection of Autism Spectrum Disorder Using ResNet: A Deep Learning Approach2024 International Conference on Data Science and Network Security (ICDSNS)10.1109/ICDSNS62112.2024.10690847(1-8)Online publication date: 26-Jul-2024
https://doi.org/10.1109/ICDSNS62112.2024.10690847
Asmetha Jeyarani RSenthilkumar RGowri RVignesh SRudhra Y(2024)Leveraging Transfer Learning with Stacked Ensemble Learning for theDetection of Autism Spectrum Disorder using Eye-tracking2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)10.1109/ACCAI61061.2024.10601827(1-8)Online publication date: 9-May-2024
https://doi.org/10.1109/ACCAI61061.2024.10601827
Molina AArroyo YLacave CRedondo MBravo COrtega M(2024)Eye tracking-based evaluation of accessible and usable interactive systems: tool set of guidelines and methodological issuesUniversal Access in the Information Society10.1007/s10209-023-01083-xOnline publication date: 11-Jan-2024
https://doi.org/10.1007/s10209-023-01083-x
Ahmed ZAlbalawi EAldhyani TJadhav MJanrao PObeidat M(2023)Applying Eye Tracking with Deep Learning Techniques for Early-Stage Detection of Autism Spectrum DisordersData10.3390/data81101688:11(168)Online publication date: 3-Nov-2023
https://doi.org/10.3390/data8110168
Córdoba-Tlaxcalteco MBenítez-Guerrero E(2023)Human Event Recognition in Smart Classrooms Using Computer Vision: A Systematic Literature ReviewProgramming and Computing Software10.1134/S036176882308006649:8(625-642)Online publication date: 1-Dec-2023
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Reethika APriyadharsini SVijaykumar GRasheedha ADevi GPriya P(2023)Enhancing Pervasive Developmental Disorder Interventions for Children through Deep Learning Algorithms in Virtual Reality2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS)10.1109/ICACRS58579.2023.10405338(902-907)Online publication date: 11-Dec-2023
https://doi.org/10.1109/ICACRS58579.2023.10405338
Asmetha Jeyarani RSenthilkumar R(2023)Eye Tracking Biomarkers for Autism Spectrum Disorder Detection using Machine Learning and Deep Learning Techniques: ReviewResearch in Autism Spectrum Disorders10.1016/j.rasd.2023.102228108(102228)Online publication date: Oct-2023
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