DocumentCode
3468902
Title
Self-Stimulatory Behaviours in the Wild for Autism Diagnosis
Author
Rajagopalan, Shyam Sundar ; Dhall, Abhinav ; Goecke, Roland
Author_Institution
HCC Lab., Univ. of Canberra, Canberra, ACT, Australia
fYear
2013
fDate
2-8 Dec. 2013
Firstpage
755
Lastpage
761
Abstract
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.
Keywords
behavioural sciences computing; neurophysiology; paediatrics; patient diagnosis; social sciences computing; video signal processing; ASD; autism diagnosis; autism spectrum disorders; children videos; cognitive skills; communicative behaviours; neurological disorders; self-stimulatory behaviours; social behaviours; Algorithm design and analysis; Autism; Computer vision; Magnetic heads; Sensors; Variable speed drives; Videos; Action Recognition; Autism Spectrum Disorder; Computational Behaviour Analysis; Dataset; Stimming;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision Workshops (ICCVW), 2013 IEEE International Conference on
Conference_Location
Sydney, NSW
Type
conf
DOI
10.1109/ICCVW.2013.103
Filename
6755972
Link To Document