自闭症
心理学
共同注意
自闭症谱系障碍
发展心理学
认知心理学
感知
特征提取
杠杆(统计)
认知
人工智能
模式识别(心理学)
计算机科学
神经科学
作者
Jingjing Liu,Zhiyong Wang,Haibo Qin,Yi Wang,Jingxin Deng,Huiping Li,Qiong Xu,Qiong Xu,Honghai Liu
出处
期刊:IEEE Transactions on Biomedical Engineering
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:71 (1): 237-246
标识
DOI:10.1109/tbme.2023.3296489
摘要
Autism Spectrum Disorders (ASD) are characterized by impairments in joint attention (JA) comprising two components: responding to JA (RJA) and initiating JA (IJA). RJA and IJA are considered two interrelated aspects of JA, related to different stages of infant development. While recent technologies have been used to characterize RJA emerging in earlier childhood, only a limited number of studies have attempted to explore IJA, which progressively becomes evident as a hallmark of ASD. This study aims to achieve the social recognition of both RJA and IJA by vision-based human behavior perception through a multi-modal framework automatically and comprehensively.The first three layers of this framework leverage localization, feature extraction, and activity recognition. On this basis, three critical activities in JA are recognized: attention estimation, spontaneous pointing, and showing actions. Then different behaviors are linked through the fourth layer, semantic interpretation, to model the JA event. The proposed framework is evaluated on experiments of four groups: 7 children with ASD, 5 children with mental retardation (MR), 5 children with developmental language disorder (DLD), and 3 typically developed children (TD).Experimental results compared with human codings demonstrate recognition reliability with an intra-class coefficient of 0.959. In addition, statistical analysis suggests significant group difference and correlations.The multi-modal human behavior perception-based framework is a feasible solution for the recognition of joint attention in unconstrained environments.Thus the proposed approach has the potential to improve the clinical diagnosis of autism by offering quantitative monitoring and statistical analysis.
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