Predicting Core Characteristics of ASD Through Facial Emotion Recognition and Eye Tracking in Youth

Ming Jiang; Sunday Francis; Angela Tseng; Diksha Srishyla; Megan Dubois; Katie Beard; Christine A Conelea; Qi Zhao; Suma Jacob

doi:10.1109/EMBC44109.2020.9176843

Predicting Core Characteristics of ASD Through Facial Emotion Recognition and Eye Tracking in Youth

Ming Jiang, Sunday Francis, Angela Tseng, Diksha Srishyla, Megan Dubois, Katie Beard, Christine A Conelea, Qi Zhao, Suma Jacob

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

17 Scopus citations

Abstract

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder (NDD) with a high rate of comorbidity. The implementation of eye-tracking methodologies has informed behavioral and neurophysiological patterns of visual processing across ASD and comorbid NDDs. In this study, we propose a machine learning method to predict measures of two core ASD characteristics: impaired social interactions and communication, and restricted, repetitive, and stereotyped behaviors and interests. Our method extracts behavioral features from task performance and eye-tracking data collected during a facial emotion recognition paradigm. We achieved high regression accuracy using a Random Forest regressor trained to predict scores on the SRS-2 and RBS-R assessments; this approach may serve as a classifier for ASD diagnosis.

Original language	English (US)
Title of host publication	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Enabling Innovative Technologies for Global Healthcare, EMBC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	871-875
Number of pages	5
Volume	2020
ISBN (Electronic)	9781728119908
DOIs	https://doi.org/10.1109/EMBC44109.2020.9176843
State	Published - Jul 2020
Event	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada Duration: Jul 20 2020 → Jul 24 2020

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2020-July
ISSN (Print)	1557-170X

Conference

Conference	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Country/Territory	Canada
City	Montreal
Period	7/20/20 → 7/24/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Adolescent
Autism Spectrum Disorder/diagnosis
Emotions
Face
Facial Recognition
Humans
Social Communication Disorder

PubMed: MeSH publication types

Journal Article

Publisher link

10.1109/EMBC44109.2020.9176843

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Jiang, M., Francis, S., Tseng, A., Srishyla, D., Dubois, M., Beard, K., Conelea, C. A., Zhao, Q., & Jacob, S. (2020). Predicting Core Characteristics of ASD Through Facial Emotion Recognition and Eye Tracking in Youth. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020 (Vol. 2020, pp. 871-875). Article 9176843 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC44109.2020.9176843

Predicting Core Characteristics of ASD Through Facial Emotion Recognition and Eye Tracking in Youth. / Jiang, Ming; Francis, Sunday; Tseng, Angela et al.
42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Vol. 2020 Institute of Electrical and Electronics Engineers Inc., 2020. p. 871-875 9176843 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jiang, M, Francis, S, Tseng, A, Srishyla, D, Dubois, M, Beard, K, Conelea, CA , Zhao, Q & Jacob, S 2020, Predicting Core Characteristics of ASD Through Facial Emotion Recognition and Eye Tracking in Youth. in 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. vol. 2020, 9176843, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2020-July, Institute of Electrical and Electronics Engineers Inc., pp. 871-875, 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada, 7/20/20. https://doi.org/10.1109/EMBC44109.2020.9176843

Jiang M, Francis S, Tseng A, Srishyla D, Dubois M, Beard K et al. Predicting Core Characteristics of ASD Through Facial Emotion Recognition and Eye Tracking in Youth. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Vol. 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 871-875. 9176843. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC44109.2020.9176843

Jiang, Ming ; Francis, Sunday ; Tseng, Angela et al. / Predicting Core Characteristics of ASD Through Facial Emotion Recognition and Eye Tracking in Youth. 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Vol. 2020 Institute of Electrical and Electronics Engineers Inc., 2020. pp. 871-875 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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abstract = "Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder (NDD) with a high rate of comorbidity. The implementation of eye-tracking methodologies has informed behavioral and neurophysiological patterns of visual processing across ASD and comorbid NDDs. In this study, we propose a machine learning method to predict measures of two core ASD characteristics: impaired social interactions and communication, and restricted, repetitive, and stereotyped behaviors and interests. Our method extracts behavioral features from task performance and eye-tracking data collected during a facial emotion recognition paradigm. We achieved high regression accuracy using a Random Forest regressor trained to predict scores on the SRS-2 and RBS-R assessments; this approach may serve as a classifier for ASD diagnosis.",

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author = "Ming Jiang and Sunday Francis and Angela Tseng and Diksha Srishyla and Megan Dubois and Katie Beard and Conelea, {Christine A} and Qi Zhao and Suma Jacob",

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Predicting Core Characteristics of ASD Through Facial Emotion Recognition and Eye Tracking in Youth

Abstract

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Keywords

PubMed: MeSH publication types

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