Autism Detection Based on Eye Movement Sequences on the Web: A Scanpath Trend Analysis Approach

We propose a novel approach to detect autism based on the eye-movement paths of users on the Web. This approach based on Scanpath Trend Analysis (STA) proposed by Eraslan et al. (2016, 2017). This dataset is created to provide supplementary data for our paper entitled "Autism Detection Based on Eye Movement Sequences on the Web: A Scanpath Trend Analysis Approach" presented at the 17th International Web for All Conference (W4A'20). This dataset includes all the individual paths used for the evaluation of our proposed approach. The dataset also includes the Python code to re-run the evaluation.

References:

Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2016. Scanpath Trend Analysis on Web Pages: Clustering Eye Tracking Scanpaths. ACM Transactions on the Web, (SCI-E), 10, 4, Article 20.

Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2017. Engineering web-based interactive systems: trend analysis in eye tracking scanpaths with a tolerance. In Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS '17). ACM, New York, NY, USA, 3-8.