Abstract

Autism spectrum disorder is a group of neurodevelopmental disorders. The disease's etiology is unclear, and there is no specific drug treatment for the core symptoms of autism spectrum disease. The study aims to explore effective intervention methods for children with autism spectrum disorders.This paper proposes a visual strategy intervention method for children with autism spectrum disorders. This method combines feature extraction and abnormal behavior detection and can use a visual cue strategy to integrate children into social groups. Firstly, the spatial-temporal feature fusion structure is added to extract behavioral features from children, and the spatial information contained in MotionNet is fused with temporal features. Optical Flow Feature (OFF) subnetwork is added to the optical flow extraction feature network. Each layer feature is input to the OFF subnet to extract the time feature further. Then, a behavior detection method based on the sequential pool is proposed. This method combines attention mechanism and clustering pool to effectively describe human behavior dynamics in the long, redundant video under complex background. Finally, feature extraction and behavior detection experiments are carried out on SDUFall, Weizmann, and HMDB51 data sets.The model's accuracy is still slightly higher than others in that only the video Red-Green-Blue (RGB) frame is used as input. Compared with OFF, SDUFall can reach 88.64%, and HMDB51 can only reach 63.81%. In contrast, the proposed model can reach 72.09%, higher than others. The descriptor obtained the best result of 92.57%, which is 3.64%, 2.58%, and 1.73% higher than the other three comparison descriptors. The data show that the method presented here is effective and has advantages in detecting children's abnormal behavior.This method and visual intervention for children with autism spectrum disorders can help them to overcome social barriers.

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