Self-stimulatory behaviours in the wild for autism diagnosis

Shyam Sundar Rajagopalan, Abhinav Dhall, Roland Goecke

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

21 Citations (Scopus)


Autism Spectrum Disorders (ASD), often referred to as autism, are neurological disorders characterised by deficits in cognitive skills, social and communicative behaviours. A common way of diagnosing ASD is by studying behavioural cues expressed by the children. We introduce a new publicly-available dataset of children videos exhibiting self-stimulatory (stimming) behaviours commonly used for autism diagnosis. These videos, posted by parents/caregivers in public domain websites, are collected and annotated for the stimming behaviours. These videos are extremely challenging for automatic behaviour analysis as they are recorded in uncontrolled natural settings. The dataset contains 75 videos with an average duration of 90 seconds per video, grouped under three categories of stimming behaviours: arm flapping, head banging and spinning. We also provide baseline results of tests conducted on this dataset using a standard bag of words approach for human action recognition. To the best of our knowledge, this is the first attempt in publicly making available a Self-Stimulatory Behaviour Dataset (SSBD) of children videos recorded in natural settings.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Computer Vision Workshops, ICCVW 2013
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Print)9781479930227
Publication statusPublished - 1 Jan 2013
Externally publishedYes
EventIEEE International Conference on Computer Vision Workshops 2013 - Sydney, Australia
Duration: 1 Dec 20138 Dec 2013
Conference number: 14th

Publication series

NameProceedings of the IEEE International Conference on Computer Vision


ConferenceIEEE International Conference on Computer Vision Workshops 2013
Abbreviated titleICCVW 2013


  • Action recognition
  • Autism spectrum disorder
  • Computational behaviour analysis
  • Dataset
  • Stimming

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