2017 |
Yaakob, A D A; Ruhaiyem, N I R Measuring the variabilities in the body postures of the children for early detection of autism spectrum disorder (ASD) Journal Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10645 LNCS , pp. 510-520, 2017, ISSN: 03029743, (cited By 0). Abstract | Links | BibTeX | Tags: Arm Flapping, Asymmetry Measurements, Autism Spectrum Disorders, Body Postures, Computational Framework, Diseases, Human Action Recognition, Musculoskeletal System, Skeletal Representation, Stereotyped Behaviour @article{Yaakob2017510, title = {Measuring the variabilities in the body postures of the children for early detection of autism spectrum disorder (ASD)}, author = {A D A Yaakob and N I R Ruhaiyem}, editor = {Robinson Smeaton Terutoshi Badioze Zaman Jaafar Mohamad Ali P A F T H A N Shih T.K. Velastin S.}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035094080&doi=10.1007%2f978-3-319-70010-6_47&partnerID=40&md5=c2eca5301a2ddf03218e9d47feedbed0}, doi = {10.1007/978-3-319-70010-6_47}, issn = {03029743}, year = {2017}, date = {2017-01-01}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {10645 LNCS}, pages = {510-520}, publisher = {Springer Verlag}, abstract = {Presently, the number of children with autism appears to be growing at disturbing rate. Unfortunately, the awareness of early sign of Autism Spectrum Disorder (ASD) is still insufficiently provided to the public. Arm flapping is a good example of a stereotypical behavior of ASD early sign. Typically, a standard Repetitive Behavior Scale-Revised (RBSR) - set of questionnaire - used by clinicians for ASD diagnosis usually involved multiple and long sessions that apparently would delay and may have nonconformity. Thus, we aim to propose a computational framework to semi-automate the diagnosis process. We used human action recognition (HAR) algorithm. HAR involved in human body detection and the skeleton representation to show the arm asymmetrical in arm flapping movement which indicates the possibility of ASD signs by extracting the body pose into stickman model. The proposed framework has been tested against the video clips of children performing arm flapping behavior taken from public dataset. The outcome of this study is expected to detect early sign of ASD based on asymmetry measurement of arm flapping behavior. © Springer International Publishing AG 2017.}, note = {cited By 0}, keywords = {Arm Flapping, Asymmetry Measurements, Autism Spectrum Disorders, Body Postures, Computational Framework, Diseases, Human Action Recognition, Musculoskeletal System, Skeletal Representation, Stereotyped Behaviour}, pubstate = {published}, tppubtype = {article} } Presently, the number of children with autism appears to be growing at disturbing rate. Unfortunately, the awareness of early sign of Autism Spectrum Disorder (ASD) is still insufficiently provided to the public. Arm flapping is a good example of a stereotypical behavior of ASD early sign. Typically, a standard Repetitive Behavior Scale-Revised (RBSR) - set of questionnaire - used by clinicians for ASD diagnosis usually involved multiple and long sessions that apparently would delay and may have nonconformity. Thus, we aim to propose a computational framework to semi-automate the diagnosis process. We used human action recognition (HAR) algorithm. HAR involved in human body detection and the skeleton representation to show the arm asymmetrical in arm flapping movement which indicates the possibility of ASD signs by extracting the body pose into stickman model. The proposed framework has been tested against the video clips of children performing arm flapping behavior taken from public dataset. The outcome of this study is expected to detect early sign of ASD based on asymmetry measurement of arm flapping behavior. © Springer International Publishing AG 2017. |
2016 |
Muty, N; Azizul, Z Institute of Electrical and Electronics Engineers Inc., 2016, ISBN: 9781509016365, (cited By 2). Abstract | Links | BibTeX | Tags: 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 @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} } 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. |
Testingadminnaacuitm2020-05-28T06:49:14+00:00
2017 |
Measuring the variabilities in the body postures of the children for early detection of autism spectrum disorder (ASD) Journal Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10645 LNCS , pp. 510-520, 2017, ISSN: 03029743, (cited By 0). |
2016 |
Institute of Electrical and Electronics Engineers Inc., 2016, ISBN: 9781509016365, (cited By 2). |