Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism
This work addresses the need for clinical decision support systems to reduce diagnosis delays in autism spectrum disorders, though it is incremental as it builds on existing methods for behavior analysis.
The authors tackled the problem of automating the screening of autism-related behaviors in children by proposing a non-intrusive approach using skeleton keypoint identification from video clips, achieving best performance with a decision tree classifier on their newly contributed database.
This paper has been withdrawn by the authors due to insufficient or definition error(s) in the ethics approval protocol.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.