An intelligent multimodal framework for identifying children with autism spectrum disorder

• Autorzy: Chen Jingying , Liao Mengyi , Wang Guangshuai , Chen Chang

Identyfikatory

DOI

10.34768/amcs-2020-0032

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

autism spectrum disorder; eye fixation; facial expression; cognitive level; improved random forest

PL

spektrum zaburzeń autystycznych; wyraz twarzy; poziom poznawczy; las losowy

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2020
• Tom: Vol. 30, no. 3
• Strony: 435--448
• Opis fizyczny: Bibliogr. 49 poz., rys., tab.

Twórcy

autor

Chen Jingying

• National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, Hubei, China; National Engineering Laboratory for Technology of Big Data Application in Education, Central China Normal University, Wuhan 430079, Hubei, China

autor

Liao Mengyi

• National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, Hubei, China; College of Computer Science and Technology, Pingdingshan University, Pingdingshan 467000, Henan, China

autor

Wang Guangshuai

• National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, Hubei, China

autor

Chen Chang

• National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, Hubei, China

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).