Automatic detection of stereotyped movements in autistic children using the Kinect sensor

Autism Spectrum Disorders (ASD) is a developmental disorder that affects communications, social skills or behaviours that can occur in some people. Children or adults with ASD often have repetitive motor movements or unusual behaviours. The objective of this work is to automatically detect stereotypical motor movements in real time using Kinect sensor. The approach is based on the $P Point-Cloud Recogniser to identify multi-stroke gestures as point clouds. This paper presents new methodology to automatically detect five stereotypical motor movements: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. With many ASD-children, our proposed system gives us satisfactory results. This can help to implement a smart video surveillance system and then helps clinicians in the diagnosing ASD.