Abstract
The current state of computer vision methods applied to autism spectrum disorder (ASD) research has not been well established. Increasing evidence suggests that computer vision techniques have a strong impact on autism research. The primary objective of this systematic review is to examine how computer vision analysis has been useful in ASD diagnosis, therapy and autism research in general. A systematic review of publications indexed on PubMed, IEEE Xplore and ACM Digital Library was conducted from 2009 to 2019. Search terms included [‘autis*’ AND (‘computer vision’ OR ‘behavio* imaging’ OR ‘behavio* analysis’ OR ‘affective computing’)]. Results are reported according to PRISMA statement. A total of 94 studies are included in the analysis. Eligible papers are categorised based on the potential biological/behavioural markers quantified in each study. Then, different computer vision approaches that were employed in the included papers are described. Different publicly available datasets are also reviewed in order to rapidly familiarise researchers with datasets applicable to their field and to accelerate both new behavioural and technological work on autism research. Finally, future research directions are outlined. The findings in this review suggest that computer vision analysis is useful for the quantification of behavioural/biological markers which can further lead to a more objective analysis in autism research.
Highlights
Visual observation and analysis of children’s natural behaviours are instrumental to the early detection of developmental disorders, including autism spectrum disorder (ASD)
Eligibility criteria All titles and abstracts were initially screened to include studies that meet the following inclusion criteria: (1) the study focussed on autism in humans; (2) the study mainly focussed on the use of computer vision techniques in autism diagnosis study, therapy of autism or autism research in general; (3) the study explained how behavioural/biological markers can be automatically quantified; and (4) the study included an experiment, a pilot study or a trial with at least one group of individuals with ASD
This review presents consolidated evidence on the effectiveness of using computer vision techniques in (1) determining behavioural/biological markers for diagnosis and characterisation of ASD, (2) developing assistive technologies that aid in emotion recognition and expression for ASD individuals and (3) augmenting existing clinical protocols with vision-based systems for ASD therapy and automatic behaviour analysis
Summary
Visual observation and analysis of children’s natural behaviours are instrumental to the early detection of developmental disorders, including autism spectrum disorder (ASD). While a gold standard observational tool is available, there are limitations that hinder the early screening of ASD in children. Interpretative coding of child observations, parent interviews and manual testing[1] are costly and time-consuming[2]. The reliability and validity of the results obtained from a clinician’s observations can be subjective[3], arising from differences in professional training, resources and cultural context. Behavioural ratings typically do not capture data from the children in their natural environments.
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