Detecting High-Functioning Autism in Adults Using Eye Tracking and Machine Learning.

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.

Less Users More Confidence: How AOIs Don't Affect Scanpath Trend Analysis.

User studies are typically difficult, recruiting enough users is often problematic and each experiment takes a considerable amount of time to be completed. In these studies, eye tracking is increasingly used which often increases time, therefore, the lower the number of users required for these studies the better for making these kinds of studies more practical in terms of economics and time expended. The possibility of achieving almost the same results with fewer users has already been raised.

Emerging technologies and web accessibility: research challenges and opportunities focussing on vision issues.

This is a technological review paper focussed on identifying both the research challenges and opportunities for further investigation arising from emerging technologies, and it does not aim to propose any recommendation or standard. It is focussed on blind and partially sighted World Wide Web (Web) users along with others who use assistive technologies. The Web is a fast moving interdisciplinary domain in which new technologies, techniques and research is in perpetual development.