NDLI: Efficient autism spectrum disorder prediction with eye movement: A machine learning framework

Content Provider	IEEE Xplore Digital Library
Author	Wenbo Liu Zhiding Yu Raj, B. Li Yi Xiaobing Zou Ming Li
Copyright Year	2015
Description	Author affiliation: SYSU-CMU Joint Inst. of Eng., Sun Yat-sen Univ., Guangzhou, China (Wenbo Liu) \|\| Dept. of Psychol., Peking Univ., Beijing, China (Li Yi) \|\| Third Affiliated Hosp., Sun Yat-sen Univ., Guangzhou, China (Xiaobing Zou) \|\| Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA (Zhiding Yu) \|\| Language Technol. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA (Raj, B.) \|\| SYSU-CMU Joint Res. Inst., Shunde, China (Ming Li)
Abstract	We propose an autism spectrum disorder (ASD) prediction system based on machine learning techniques. Our work features the novel development and application of machine learning methods over traditional ASD evaluation protocols. Specifically, we are interested in discovering the latent patterns that possibly indicate the symptom of ASD underneath the observations of eye movement. A group of subjects (either ASD or non-ASD) are shown with a set of aligned human face images, with eye gaze locations on each image recorded sequentially. An image-level feature is then extracted from the recorded eye gaze locations on each face image. Such feature extraction process is expected to capture discriminative eye movement patterns related to ASD. In this work, we propose a variety of feature extraction methods, seeking to evaluate their prediction performance comprehensively. We further propose an ASD prediction framework in which the prediction model is learned on the labeled features. At testing stage, a test subject is also asked to view the face images with eye gaze locations recorded. The learned model predicts the image-level labels and a threshold is set to determine whether the test subject potentially has ASD or not. Despite the inherent difficulty of ASD prediction, experimental results indicates statistical significance of the predicted results, showing promising perspective of this framework.
Starting Page	649
Ending Page	655
File Size	1777336
Page Count	7
File Format	PDF
ISSN	21568111
e-ISBN	9781479999538
DOI	10.1109/ACII.2015.7344638
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Histograms Visualization Dictionaries Feature extraction Bag-of-words Support vector machine Face Kernel Eye tracking Autism spectrum disorder
Content Type	Text
Resource Type	Article

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