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Autism and the web: using web-searching tasks to detect autism and improve web accessibility
Yaneva, Victoria
Yaneva, Victoria
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2018-08-02
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Abstract
People with autism consistently exhibit different attention-shifting patterns compared to neurotypical people. Research has shown that these differences can be successfully captured using eye tracking. In this paper, we summarise our recent research on using gaze data from web-related tasks to address two problems: improving web accessibility for people with autism and detecting autism automatically. We first examine the way a group of participants with autism and a control group process the visual information from web pages and provide empirical evidence of different visual searching strategies. We then use these differences in visual attention, to train a machine learning classifier which can successfully use the gaze data to distinguish between the two groups with an accuracy of 0.75. At the end of this paper we review the way forward to improving web accessibility and automatic autism detection, as well as the practical implications and alternatives for using eye tracking in these research areas.
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Yaneva, V. (2018) Autism and the web: using web-searching tasks to detect autism and improve web accessibility, ACM SIGACCESS Accessibility and Computing, 121.
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Journal article
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en
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This is an accepted manuscript of an article published by ACM in ACM SIGACCESS Accessibility and Computing on 02/08/2018, available online: https://doi.org/10.1145/3264631.3264633
The accepted version of the publication may differ from the final published version.
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ISSN
1558-2337
EISSN
1558-1187