Computational behaviour modelling for autism diagnosis

  • Authors:
  • Shyam Sundar Rajagopalan

  • Affiliations:
  • University of Canberra, Canberra, Australia

  • Venue:
  • Proceedings of the 15th ACM on International conference on multimodal interaction
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Autism Spectrum Disorders (ASD), often referred to as autism, are neurological disorders characterised by deficits in cognitive skills, social and communicative behaviours. ASD develop in early childhood and include a spectrum of related problems, such as Asperger Syndrome, Autistic Disorder, and Pervasive Development Disorder. A common way of diagnosing ASD is by studying behavioural cues expressed by the children. The focus of my PhD project is to model the common atypical behaviour cues of children suffering from ASD. These models could assist clinicians in diagnosing autism and alert parents/caregivers for early intervention. The behaviours will be studied in a discrete manner, in the context of a social dyadic conversational setting, using visual and speech signals as well as a fusion of multiple modalities. As part of my initial work, an algorithm based on dense trajectories and the short-time fourier transform is proposed for modelling stimming (repetitive) behaviour. To validate the approach, preliminary experiments are performed on human action recognition datasets that contain repetitive behaviours. In addition, publicly available videos of children exhibiting repetitive behaviours were also used.