Authors:
Federica Cilia
1
;
Romuald Carette
2
;
Mahmoud Elbattah
3
;
4
;
Jean-Luc Guérin
3
and
Gilles Dequen
3
Affiliations:
1
Laboratoire CRP-CPO, Université de Picardie Jules Verne, Amiens, France
;
2
Evolucare Technologies, Villers-Bretonneux, France
;
3
Laboratoire MIS, Université de Picardie Jules Verne, Amiens, France
;
4
Faculty of Environment and Technology, University of the West of England, Bristol, U.K.
Keyword(s):
Autism Spectrum Disorder, ASD, Eye-tracking, Machine Learning.
Abstract:
The availability of data is a key enabler for researchers across different disciplines. However, domains, such as healthcare, are still fundamentally challenged by the paucity and imbalance of datasets. Health data could be inaccessible due to a variety of hurdles such as privacy concerns, or lack of sharing incentives. In this regard, this study aims to publish an eye-tracking dataset developed for the purpose of autism diagnosis. Eye-tracking methods are used intensively in that context, whereas abnormalities of the eye gaze are largely recognised as the hallmark of autism. As such, it is believed that the dataset can allow for developing useful applications or discovering interesting insights. As well, Machine Learning is a potential application for developing diagnostic models that can help detect autism at an early stage of development.