EDA and EyeCrowdata published at ETWEB2020 – co-located event at ETRA2020!

Our two eye-tracking related projects with our undergraduate students at METU NCC have been published at ETWEB2020 – co-located event at ETRA2020.

  • Abdulrahman Zakrt, Abdulmalik Obaidah Elmahgiubi, Beshir Alhomsi, Sukru Eraslan and Yeliz Yesilada, Eye-tracking Data Analyser (EDA): Web Application and Evaluation, ETRA ’20 Adjunct: ACM Symposium on Eye Tracking Research and ApplicationsJune 2020 Article No.: 27 Pages 1–9, DOI: 10.1145/3379157.3391301
  • Naziha Shekh.Khalil, Ecem Dogruer, Abdulmohimen K. O. Elosta, Sukru Eraslan, Yeliz Yesilada and Simon Harper, EyeCrowdata: Towards a Web-based Crowdsourcing Platform for Web-related Eye-Tracking Data, ETRA ’20 Adjunct: ACM Symposium on Eye Tracking Research and ApplicationsJune 2020 Article No.: 31 Pages 1–6, DOI: 10.1145/3379157.3391304

Our autism detection work published at IEEE TNSRE!

We are very pleased that our paper on detecting high-functioning autism in adults by using eye tracking and machine learning has been published at IEEE Transactions on Neural Systems and Rehabilitation Engineering!

Victoria Yaneva, Le An Ha, Sukru Eraslan, Yeliz Yesilada, and Ruslan Mitkov. 2020. Detecting High-functioning Autism in Adults Using Eye Tracking and Machine Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering (SCI-E), 28 , 6 , 1254-1261. DOI: 10.1109/TNSRE.2020.2991675

Abstract: The purpose of this study is to test whether visual processing differences between adults with and without high-functioning autism captured through eye tracking can be used to detect autism. We record the eye movements of adult participants with and without autism while they look for information within web pages. We then use the recorded eye-tracking data to train machine learning classifiers to detect the condition. The data was collected as part of two separate studies involving a total of 71 unique participants (31 with autism and 40 control), which enabled the evaluation of the approach on two separate groups of participants, using different stimuli and tasks. We explore the effects of a number of gaze-based and other variables, showing that autism can be detected automatically with around 74% accuracy. These results confirm that eye-tracking data can be used for the automatic detection of high-functioning autism in adults and that visual processing differences between the two groups exist when processing web pages.

“The Best of Both Worlds!” published at ACM TWEB!

We are very pleased that our paper on integrating web page and eye tracking data driven approaches for automatic areas of interest detection has been published at ACM Transactions on the Web!

Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2020. “The Best of Both Worlds!”: Integration of Web Page and Eye Tracking Data Driven Approaches for Automatic AOI Detection. ACM Transactions on the Web (SCI-E), 14, 1, Article 1. DOI: 10.1145/3372497

Abstract: Web pages are composed of different kinds of elements (menus, adverts, etc.). Segmenting pages into their elements has long been important in understanding how people experience those pages and in making those experiences “better.” Many approaches have been proposed that relate the resultant elements with the underlying source code; however, they do not consider users’ interactions. Another group of approaches analyses eye movements of users to discover areas that interest or attract them (i.e., areas of interest or AOIs). Although these approaches consider how users interact with web pages, they do not relate AOIs with the underlying source code. We propose a novel approach that integrates web page and eye tracking data driven approaches for automatic AOI detection. This approach segments an entire web page into its AOIs by considering users’ interactions and relates AOIs with the underlying source code. Based on the Adjusted Rand Index measure, our approach provides the most similar segmentation to the ground-truth segmentation compared to its individual components.

“Keep it Simple” published at UAIS!

We are very pleased that our paper on the exploration of the complexity and distinguishability of web pages for people with autism has been published at Universal Access in the Information Society!

Sukru Eraslan, Yeliz Yesilada, Victoria Yaneva and Le An Ha. 2020. “Keep it Simple!” An Eye-tracking Study for Exploring Complexity and Distinguishability of Web Pages for People with Autism. Universal Access in the Information Society (SCI-E, SSCI). DOI: 10.1007/s10209-020-00708-9

Abstract: A major limitation of the international well-known standard web accessibility guidelines for people with cognitive disabilities is that they have not been empirically evaluated by using relevant user groups. Instead, they aim to anticipate issues that may arise following the diagnostic criteria. In this paper, we address this problem by empirically evaluating two of the most popular guidelines related to the visual complexity of web pages and the distinguishability of web-page elements. We conducted a comparative eye-tracking study with 19 verbal and highly independent people with autism and 19 neurotypical people on eight web pages with varying levels of visual complexity and distinguishability, with synthesis and browsing tasks. Our results show that people with autism have a higher number of fixations and make more transitions with synthesis tasks. When we consider the number of elements which are not related to given tasks, our analysis shows that they look at more irrelevant elements while completing the synthesis task on visually complex pages or on pages whose elements are not easily distinguishable. To the best of our knowledge, this is the first empirical behavioural study which evaluates these guidelines by showing that the high visual complexity of pages or the low distinguishability of page elements causes non-equivalent experience for people with autism.

 

W4A2020 Presentations + Best Technical Paper Award!

We have three papers presented at W4A2020 which are:

  • Sukru Eraslan, Yeliz Yesilada, Victoria Yaneva and Simon Harper, Autism detection based on eye movement sequences on the web: a scanpath trend analysis approach, W4A ’20: Proceedings of the 17th International Web for All Conference, Article No.: 11 Pages 1–10, 2020,DOI: https://doi.org/10.1145/3371300.3383340 [Best Technical Paper].
  • Waqar Haider and Yeliz Yesilada, Tables on the web accessible?: unfortunately not! W4A ’20: Proceedings of the 17th International Web for All Conference, Article No.: 7 Pages 1–5, 2020, DOI: https://doi.org/10.1145/3371300.3383349
  • Simon Harper, Julia Mueller, Alan Davies, Hugo Nicolau, Sukru Eraslan, The case for ‘health related impairments and disabilities, W4A ’20: Proceedings of the 17th International Web for All Conference, Article No.: 2 Pages 1–7, 2020, DOI: https://doi.org/10.1145/3371300.3383335

We are very pleased that our paper on autism detection based on eye movement seqeuences on the web recieved the best technical paper award!

Web4All 2018 Presentations

We have two papers presented at Web4All which are:

We are very pleased to announce that our second paper received the best technical paper award!