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

Authors: Maha Jazouli; Aicha Majda; Djamal Merad; Rachid Aalouane; Arsalane Zarghili

Addresses: Laboratory of Intelligent Systems & Applications, Faculty of Science and Technology, Sidi Mohamed Benabdellah University, Fez, Morocco ' Laboratory of Intelligent Systems & Applications, Faculty of Science and Technology, Sidi Mohamed Benabdellah University, Fez, Morocco ' Laboratory of Information Science and Systems, Aix-Marseille University, Marseille, France ' Laboratory of Clinical Neurosciences, CHU HASSAN II Hospital, Faculty of Medicine and Pharmacy, Sidi Mohamed Benabdellah University, Fez, Morocco ' Laboratory of Intelligent Systems & Applications, Faculty of Science and Technology, Sidi Mohamed Benabdellah University, Fez, Morocco

Abstract: 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.

Keywords: ASD; autism; stereotyped movement; repetitive motor movements; repetitive behaviours; gesture detection; Kinect sensor; point cloud; nearest-neighbour classifier; gesture recognition.

DOI: 10.1504/IJBET.2019.097621

International Journal of Biomedical Engineering and Technology, 2019 Vol.29 No.3, pp.201 - 220

Available online: 18 Jan 2019 *

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