@conference{Razali2010,
title = {Motor movement for autism spectrum disorder (ASD) detection},
author = {N Razali and A W A Rahman},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052346152&doi=10.1109%2fICT4M.2010.5971921&partnerID=40&md5=234cdd8f3906ad980ed163a1036215ee},
doi = {10.1109/ICT4M.2010.5971921},
isbn = {9789791948913},
year = {2010},
date = {2010-01-01},
journal = {Proceeding of the 3rd International Conference on Information and Communication Technology for the Moslem World: ICT Connecting Cultures, ICT4M 2010},
pages = {E90-E95},
abstract = {In this paper, we are looking at the differences between autistic and normal children in term of fine motor movement. Previous findings have shown that there are differences between autistic children and normal children when performing a simple motor movement tasks. Imitating a finger tapping and clinching a hand are two examples of a simple motor movement tasks. Our study had adopted one of the video stimuli for clinching the hand from Brainmarkers. 6 selected autistic children and 6 selected normal children were involved in this study. The data collection is using EEG device and will be analyzed using Gaussian mixture model (GMM) and Multilayer perceptron (MLP) as classifier to discriminate between autistic and normal children. Experimental result shows the potential of verifying between autistic and normal children with accuracy of 92%. The potential of using these techniques to identify autistic children can help early detection for the purpose of early intervention. Moreover, the spectrums of the signals also present big differences between the two groups. © 2010 IEEE.},
note = {cited By 3},
keywords = {Autism, Autism Spectrum Disorders, Autistic Children, Children with Autism, Data Collection, Diseases, Early Detection, Early Intervention, Finger Tapping, Gaussian Mixture Model, Information Technology, Motor Movements, Multi Layer Perceptron, Multilayer Perceptron (MLP), Multilayers},
pubstate = {published},
tppubtype = {conference}
}