Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are neurodevelopmental conditions which impact on a significant number of children and adults. Currently, the diagnosis of such disorders is done by experts who employ standard questionnaires and look for certain behavioural markers through manual observation. Such methods for their diagnosis are not only subjective, difficult to repeat, and costly but also extremely time consuming. In this work, we present a novel methodology to aid diagnostic predictions about the presence absence of ADHD and ASD by automatic visual analysis of a person's behaviour. To do so, we conduct the questionnaires in a computer-mediated way while recording participants with modern RGBD (Colour+Depth) sensors. In contrast to previous automatic approaches which have focussed only detecting certain behavioural markers, our approach provides a fully automatic end-to-end system for directly predicting ADHD and ASD in adults. Using state of the art facial expression analysis based on Dynamic Deep Learning and 3D analysis of behaviour, we attain classification rates of 96 for Controls vs Condition (ADHD ASD) group and 94 for Comorbid (ADHD+ASD) vs ASD only group. We show that our system is a potentially useful time saving contribution to the diagnostic field of ADHD and ASD.
In @cite_0 , the authors presented another computer vision based approach for studying autism by retrieving social games and other forms of social interactions between adults and children in videos. They proposed to do this by defining social games as quasi-periodic spatio-temporal patterns. In order to retrieve such patterns from unstructured videos, the authors represent each frame using a histogram of spatio-temporal words derived from space-time interest points. The frames are clustered based on their histograms to represent the video as a sequence of cluster (keyframes) labels. The quasi-periodic pattern is found by searching for co-occurrences of these keyframe labels in time.
@cite_0
2407966565
Computer vision technology has a unique opportunity to impact that study of children’s behavior, by providing a means to automatically capture behavioral data in an noninvasive manner and analyze behavioral interactions between children and their caregivers and peers. We briefly outline a research agenda in Behavior Imaging, which targets the capture and analysis of social and communicative behaviors. We present illustrative results from an on-going project on the content-based retrieval of social games between children and adults from an unstructured video corpus.