Automatic Autism Spectrum Disorder Detection Thanks to Eye-Tracking and Neural Network-Based Approach

被引：19

作者：

Carette, Romuald ^{[1
]}

Cilia, Federica ^{[2
]}

Dequen, Gilles ^{[1
]}

Bosche, Jerome ^{[1
]}

Guerin, Jean-Luc ^{[1
]}

Vandromme, Luc ^{[2
]}

机构：

[1] Univ Picardie Jules Verne, Lab Modelisat Informat & Syst, Amiens, Picardie, France

[2] Univ Picardie Jules Verne, Ctr Rech Psychol Cognit Psychisme & Org, Amiens, Picardie, France

来源：

INTERNET OF THINGS (IOT) TECHNOLOGIES FOR HEALTHCARE, HEALTHYIOT 2017 | 2018年 / 225卷

关键词：

Neural network; Long Short-Term Memory (LSTM); Data processing; Eye-tracking; Autism spectrum disorder; eHealth; HOME MOVIES; FACE;

D O I：

10.1007/978-3-319-76213-5_11

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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%.

引用

页码：75 / 81

页数：7

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