Title: Recognising of repetitive and stereotyped movements for children with autism spectrum disorder

Authors: Maha Jazouli; Soufiane Ezghari; Aicha Majda; Azeddine Zahi; Rachid Aalouane; Arsalane Zarghili

Addresses: Laboratory of Intelligent Systems & Applications (SIA), Faculty of Science and Technology, Sidi Mohamed Benabdellah University, Fez, 30050, Morocco ' Laboratory of Intelligent Systems & Applications (SIA), Faculty of Science and Technology, Sidi Mohamed Benabdellah University, Fez, 30050, Morocco ' Laboratory of Intelligent Systems & Applications (SIA), Faculty of Science and Technology, Sidi Mohamed Benabdellah University, Fez, 30050, Morocco ' Laboratory of Intelligent Systems & Applications (SIA), Faculty of Science and Technology, Sidi Mohamed Benabdellah University, Fez, 30050, Morocco ' Laboratory of Clinical Neurosciences, CHU HASSAN II Hospital, Faculty of Medicine and Pharmacy, Sidi Mohamed Benabdellah University, Fez, Morocco ' Laboratory of Intelligent Systems & Applications (SIA), Faculty of Science and Technology, Sidi Mohamed Benabdellah University, Fez, Morocco

Abstract: People with autism often engage in stereotyped and repetitive motor movements. Hence, our aim is to put out a smart video surveillance system that facilitates the diagnosis of autism for doctors. In this respect, we propose an automatic stereotypical motor movement detection system in real time. Firstly, we use the Kinect sensor to monitor the child's with autism movements. Secondly, we propose a data integration process to make the provided data from Kinect sensor more comprehensive and specific. Thirdly, we perform the gesture detection by using the well know machine learning algorithms such as decision tree, artificial neural network and nearest neighbour. The obtained result is very promising and shows that the data integration step enhances the gesture recognition. The experimental results show that our system can achieve above 99.8% recognition rate. Also, we evaluate the efficiency of different machine learning algorithms in recognition tasks.

Keywords: autism spectrum disorder; stereotypical motor movements; stereotyped behaviours; Kinect sensor; gesture recognition; machine learning.

DOI: 10.1504/IJBRA.2020.113020

International Journal of Bioinformatics Research and Applications, 2020 Vol.16 No.4, pp.355 - 372

Received: 07 Nov 2017
Accepted: 14 Apr 2018

Published online: 03 Feb 2021 *

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