Autism detection based on eye movement sequences on the web: a scanpath trend analysis approach
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AbstractAutism diagnostic procedure is a subjective, challenging and expensive procedure and relies on behavioral, historical and parental report information. In our previous, we proposed a machine learning classifier to be used as a potential screening tool or used in conjunction with other diagnostic methods, thus aiding established diagnostic methods. The classifier uses eye movements of people on web pages but it only considers non-sequential data. It achieves the best accuracy by combining data from several web pages and it has varying levels of accuracy on different web pages. In this present paper, we investigate whether it is possible to detect autism based on eye-movement sequences and achieve stable accuracy across different web pages to be not dependent on specific web pages. We used Scanpath Trend Analysis (STA) which is designed for identifying a trending path of a group of users on a web page based on their eye movements. We first identify trending paths of people with autism and neurotypical people. To detect whether or not a person has autism, we calculate the similarity of his/her path to the trending paths of people with autism and neurotypical people. If the path is more similar to the trending path of neurotypical people, we classify the person as a neurotypical person. Otherwise, we classify her/him as a person with autism. We systematically evaluate our approach with an eye-tracking dataset of 15 verbal and highly-independent people with autism and 15 neurotypical people on six web pages. Our evaluation shows that the STA approach performs better on individual web pages and provides more stable accuracy across different pages.
CitationEraslan, S., Yesilada, Y., Yaneva, V. and Harper, S. (2020) Autism detection based on eye movement sequences on the web: a scanpath trend analysis approach, in W4A '20: Proceedings of the 17th International Web for All Conference, April 2020, pp. 1–10. https://doi.org/10.1145/3371300.3383340
DescriptionThis is an accepted manuscript of an article published by ACM in W4A '20: Proceedings of the 17th International Web for All Conference on 20/04/2020, available online: https://doi.org/10.1145/3371300.3383340 The accepted version of the publication may differ from the final published version.
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/