Research Article
Automatic Autism Spectrum Disorder Detection Thanks to Eye-Tracking and Neural Network-Based Approach
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@INPROCEEDINGS{10.1007/978-3-319-76213-5_11, author={Romuald Carette and Federica Cilia and Gilles Dequen and Jerome Bosche and Jean-Luc Guerin and Luc Vandromme}, title={Automatic Autism Spectrum Disorder Detection Thanks to Eye-Tracking and Neural Network-Based Approach}, proceedings={Internet of Things (IoT) Technologies for HealthCare. 4th International Conference, HealthyIoT 2017, Angers, France, October 24-25, 2017, Proceedings}, proceedings_a={HEALTHYIOT}, year={2018}, month={2}, keywords={Neural network Long Short-Term Memory (LSTM) Data processing Eye-tracking Autism spectrum disorder eHealth}, doi={10.1007/978-3-319-76213-5_11} }- Romuald Carette
Federica Cilia
Gilles Dequen
Jerome Bosche
Jean-Luc Guerin
Luc Vandromme
Year: 2018
Automatic Autism Spectrum Disorder Detection Thanks to Eye-Tracking and Neural Network-Based Approach
HEALTHYIOT
Springer
DOI: 10.1007/978-3-319-76213-5_11
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder quite wide and its numerous variations render diagnosis hard. Some works have proven that children suffering from autism have trouble keeping their attention and tend to have a less focused sight. On top of that, eye-tracking systems enable the recording of precise eye focus on a screen. This paper deals with automatic detection of autism spectrum disorder thanks to eye-tracked data and an original Machine Learning approach. Focusing on data that describes the saccades of the patient’s sight, we distinguish, out of our six test patients, young autistic individuals from those with no problems in 83% (five) of tested patients, with a results confidence up to 95%.
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