<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Early identification can significantly improve the prognosis of children with autism spectrum disorder (ASD). Yet existing identification methods are costly, time consuming, and dependent on the manual judgment of specialists. In this study, we present a multimodal framework that fuses data on a child’s eye fixation, facial expression, and cognitive level to automatically identify children with ASD, to improve the identification efficiency and reduce costs. The proposed methodology uses an optimized random forest (RF) algorithm to improve classification accuracy and then applies a hybrid fusion method based on the data source and time synchronization to ensure the reliability of the classification results. The classification accuracy of the framework was 91%, which is higher than that of the RF, support vector machine, and discriminant analysis methods. The results suggest that data on a child’s eye fixation, facial expression, and cognitive level are useful for identifying children with ASD. Because the proposed framework can separate ASD children from typically developing (TD) children, it can facilitate the early identification of ASD and may improve intervention programs for children with ASD.</p></abstract>

Early identification can significantly improve the prognosis of children with autism spectrum disorder (ASD). Yet existing identification methods are costly, time consuming, and dependent on the manual judgment of specialists. In this study, we present a multimodal framework that fuses data on a child’s eye fixation, facial expression, and cognitive level to automatically identify children with ASD, to improve the identification efficiency and reduce costs. The proposed methodology uses an optimized random forest (RF) algorithm to improve classification accuracy and then applies a hybrid fusion method based on the data source and time synchronization to ensure the reliability of the classification results. The classification accuracy of the framework was 91%, which is higher than that of the RF, support vector machine, and discriminant analysis methods. The results suggest that data on a child’s eye fixation, facial expression, and cognitive level are useful for identifying children with ASD. Because the proposed framework can separate ASD children from typically developing (TD) children, it can facilitate the early identification of ASD and may improve intervention programs for children with ASD.

An Intelligent Multimodal Framework for Identifying Children with Autism Spectrum Disorder

National Engineering Laboratory for Technology of Big Data Application in Education

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Early identification can significantly improve the prognosis of children with autism spectrum disorder (ASD). Yet existing identification methods are costly,...

An Intelligent Multimodal Framework for Identifying Children with Autism Spectrum Disorder

Publié en ligne: 29 sept. 2020

Pages: 435 - 448

Reçu: 25 oct. 2019

Accepté: 04 juin 2020

DOI: https://doi.org/10.34768/amcs-2020-0032

Mots clés
autism spectrum disorder, eye fixation, facial expression, cognitive level, improved random forest

© 2020 Jingying Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

An Intelligent Multimodal Framework for Identifying Children with Autism Spectrum Disorder

Publié en ligne: 29 sept. 2020

Pages: 435 - 448

Reçu: 25 oct. 2019

Accepté: 04 juin 2020

DOI: https://doi.org/10.34768/amcs-2020-0032

Mots clésautism spectrum disorder, eye fixation, facial expression, cognitive level, improved random forest

© 2020 Jingying Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
autism spectrum disorder, eye fixation, facial expression, cognitive level, improved random forest