@conference{Muty2016,
title = {Detecting arm flapping in children with Autism Spectrum Disorder using human pose estimation and skeletal representation algorithms},
author = {N Muty and Z Azizul},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011297898&doi=10.1109%2fICAICTA.2016.7803118&partnerID=40&md5=e11241ced18900dbe4aab19c78c1a349},
doi = {10.1109/ICAICTA.2016.7803118},
isbn = {9781509016365},
year = {2016},
date = {2016-01-01},
journal = {4th IGNITE Conference and 2016 International Conference on Advanced Informatics: Concepts, Theory and Application, ICAICTA 2016},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Stereotypical behaviour such as arm flapping is among the prominent early signs for young children with Autism Spectrum Disorder (ASD). Diagnosis of arm flapping requires clinicians to use the standard Repetitive Behaviour Scale-Revised (RBSR) which is a structured questionnaire with the caregivers to detect the arm flapping behavioural patterns or cues. This method involves clinicians in multiple long sessions, risking a delay in diagnosis and usually an expensive process. Moreover, trained clinicians may not be available in some areas. The focus of this work is to propose a development of a computational framework to automate the diagnosis process of arm flapping. Here, we show how the human action recognition (HAR) techniques, namely, the pose estimation and the skeletal representation are utilized simultaneously to segment parts of the human body (head, neck, elbows and shoulders) into stickman model. We show how the stickman model allows us to estimate arm asymmetry (during arm flapping) which indicates possible sign of autism. The framework developed has been tested against data taken from a public database and has shown a high accuracy in detecting the repetitive behavioural pattern among young children. The results show that our method can provide efficient results in clinical assessment. © 2016 IEEE.},
note = {cited By 2},
keywords = {Arm Flapping, Autism Spectrum Disorders, Children with Autism, Computation Theory, Computational Framework, Diseases, Human Action Recognition, Human Pose Estimations, Image Recognition, Pose Estimation, Skeletal Representation},
pubstate = {published},
tppubtype = {conference}
}

@conference{Aziz201699,
title = {Proposed conceptual model of mobile numerical application for children with autism},
author = {N S A Aziz and W F W Ahmad},
editor = {Aziz Jaafar Arshad Rahim I A J N I B S K N A Abdullah M.N. Ariff M.I.B.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995543505&doi=10.1109%2fISMSC.2015.7594035&partnerID=40&md5=5fb8809b855fbcb1904d50c6ec0646b2},
doi = {10.1109/ISMSC.2015.7594035},
isbn = {9781479978946},
year = {2016},
date = {2016-01-01},
journal = {2015 International Symposium on Mathematical Sciences and Computing Research, iSMSC 2015 - Proceedings},
pages = {99-103},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Basic literacy and numeracy proficiency are two important skills which prepare and shape students for lifelong learning. This study focuses on numeracy skills for children with autism. It is crucial to conduct this study due to the increasing number of children with autism based on statistics data from the Malaysia Social Welfare Department. Children with autism have a neurological disorder that affects the functioning of the brain and causes problems with thinking, feeling, and language. Thus, a lot of studies have been conducted related to the social and literacy skills of the children with autism. However, numeracy skills are also important to the children and the advancement in mobile technology creates new methods to equip the children with numeracy skills. In order to develop a mobile numerical application that is able to fulfil the needs of these children, a proposed conceptual model will be constructed. The proposed conceptual model consists of learning theories, learning principles, multimedia elements together with colour psychology, number skills, language, gestures and contents. © 2015 IEEE.},
note = {cited By 3},
keywords = {Autism, Computation Theory, Conceptual Model, Diseases, Education, Learning, Mobile Applications, Statistics, Students},
pubstate = {published},
tppubtype = {conference}
}