Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism

TL;DR

This paper introduces a new video database of children's behaviors and a non-intrusive deep learning-based method to differentiate typical from atypical behaviors, aiding autism diagnosis.

Contribution

It provides a novel video dataset from YouTube and a preliminary skeleton-based analysis approach for behavior classification in children with autism.

Findings

Decision tree classifier performs best on the dataset.

The approach offers a baseline for future research.

The method is non-intrusive and uses pretrained neural networks.

Abstract

This paper has been withdrawn by the authors due to insufficient or definition error(s) in the ethics approval protocol. Autism spectrum disorders (ASD) impact the cognitive, social, communicative and behavioral abilities of an individual. The development of new clinical decision support systems is of importance in reducing the delay between presentation of symptoms and an accurate diagnosis. In this work, we contribute a new database consisting of video clips of typical (normal) and atypical (such as hand flapping, spinning or rocking) behaviors, displayed in natural settings, which have been collected from the YouTube video website. We propose a preliminary non-intrusive approach based on skeleton keypoint identification using pretrained deep neural networks on human body video clips to extract features and perform body movement analysis that differentiates typical and atypical behaviors of children. Experimental results on the newly contributed database show that our platform performs best with decision tree as the classifier when compared to other popular methodologies and offers a baseline against which alternate approaches may developed and tested.