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An intelligent multimodal framework for identifying children with autism spectrum disorder

100%

Chen J. , Liao M. , Wang G. , Chen C.

International Journal of Applied Mathematics and Computer Science

2020

tom Vol. 30, no. 3

435--448

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.

Design and application of facial expression analysis system in empathy ability of children with autism spectrum disorder

88%

Guo C. , Zhang K. , Chen J. , Xu R. , Gao L.

Annals of Computer Science and Information Systems

2021

tom Vol. 25

319--325

Empathy is an important social ability in early childhood development. One of the significant characteristics of children with autism spectrum disorder (ASD) is their lack of empathy, which makes it difficult for them to understand other's emotions and to judge other's behavioral intentions, leading to social disorders. This research designed and implemented a facial expression analysis system that could obtain and analyze the real-time expressions of children when viewing stimulus, and evaluate the empathy differences between ASD children and typical development children. The research results provided new ideas for evaluation of ASD children, and helped to develop empathy intervention plans.