List of Publications
There are numbers of autism related research can be found in Malaysia that generally focus on the ASD, learning disorder, communication aids, therapy and many more. The list of publications is provided below:
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2018 |
Hasan, C Z C; Jailani, R; Tahir, N M; Desaa, H M Vertical ground reaction force gait patterns during walking in children with autism spectrum disorders Journal Article International Journal of Engineering, Transactions B: Applications, 31 (5), pp. 705-711, 2018, ISSN: 1728144X, (cited By 1). Abstract | Links | BibTeX | Tags: Autism Spectrum Disorders, Biophysics, Children with Autism, Diseases, Gait Analysis, Gait Pattern, Ground Reaction Forces, Independent Samples T-Test, Mann-Whitney U Test, Parameterization Techniques, Spectrum Analysis, Three-Dimensional, Three-Dimensional Motion Analysis @article{Hasan2018705, title = {Vertical ground reaction force gait patterns during walking in children with autism spectrum disorders}, author = {C Z C Hasan and R Jailani and N M Tahir and H M Desaa}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048945706&doi=10.5829%2fije.2018.31.05b.04&partnerID=40&md5=74e349f0b128bc46da82f21d0e484d77}, doi = {10.5829/ije.2018.31.05b.04}, issn = {1728144X}, year = {2018}, date = {2018-01-01}, journal = {International Journal of Engineering, Transactions B: Applications}, volume = {31}, number = {5}, pages = {705-711}, publisher = {Materials and Energy Research Center}, abstract = {The characteristics of vertical ground reaction force (VGRF) gait patterns in children with autism spectrum disorders (ASD) are poorly understood. The purpose of this study was to identify VGRF gait features that discriminate between children with ASD and the peer control group. The VGRF data were obtained from 30 children with ASD and 30 normal healthy children aged 4 to 12 years. A three-dimensional motion analysis system with eight cameras and two force plates were used to collect VGRF data while subjects performed self-selected speed of barefoot walking. Parameterization techniques were applied to VGRF waveforms to extract the VGRF gait features. Mean significant differences between the two groups were tested using independent samples t-test and Mann-Whitney U test. Significant group differences were found for four VGRF gait features. Results indicated that children with ASD exhibited a significant reduction of the second peak of VGRF, earlier relative time to the occurrence of the second peak of VGRF, lower push-off rate, and higher peak ratio of the two VGRF peaks during normal speed of walking. These prominent differences showed that children with ASD had difficulties in supporting their body weight during terminal stance phase and these conditions affect the gait instability. The findings of this study develop further understanding of VGRF gait patterns that significantly differentiate between children with ASD and the peer control groups. © 2018 Materials and Energy Research Center. All Rights Reserved.}, note = {cited By 1}, keywords = {Autism Spectrum Disorders, Biophysics, Children with Autism, Diseases, Gait Analysis, Gait Pattern, Ground Reaction Forces, Independent Samples T-Test, Mann-Whitney U Test, Parameterization Techniques, Spectrum Analysis, Three-Dimensional, Three-Dimensional Motion Analysis}, pubstate = {published}, tppubtype = {article} } The characteristics of vertical ground reaction force (VGRF) gait patterns in children with autism spectrum disorders (ASD) are poorly understood. The purpose of this study was to identify VGRF gait features that discriminate between children with ASD and the peer control group. The VGRF data were obtained from 30 children with ASD and 30 normal healthy children aged 4 to 12 years. A three-dimensional motion analysis system with eight cameras and two force plates were used to collect VGRF data while subjects performed self-selected speed of barefoot walking. Parameterization techniques were applied to VGRF waveforms to extract the VGRF gait features. Mean significant differences between the two groups were tested using independent samples t-test and Mann-Whitney U test. Significant group differences were found for four VGRF gait features. Results indicated that children with ASD exhibited a significant reduction of the second peak of VGRF, earlier relative time to the occurrence of the second peak of VGRF, lower push-off rate, and higher peak ratio of the two VGRF peaks during normal speed of walking. These prominent differences showed that children with ASD had difficulties in supporting their body weight during terminal stance phase and these conditions affect the gait instability. The findings of this study develop further understanding of VGRF gait patterns that significantly differentiate between children with ASD and the peer control groups. © 2018 Materials and Energy Research Center. All Rights Reserved. |
2016 |
Ilias, S; Tahir, N M; Jailani, R; Hasan, C Z C Classification of autism children gait patterns using Neural Network and Support Vector Machine Conference Institute of Electrical and Electronics Engineers Inc., 2016, ISBN: 9781509015436, (cited By 5). Abstract | Links | BibTeX | Tags: Accuracy Rate, Autism, Classification (of information), Diseases, Gait Analysis, Gait Parameters, Gait Pattern, Industrial Electronics, Kinematics, Neural Networks, NN Classifiers, Sensitivity and Specificity, Support Vector Machines, Three Categories @conference{Ilias201652, title = {Classification of autism children gait patterns using Neural Network and Support Vector Machine}, author = {S Ilias and N M Tahir and R Jailani and C Z C Hasan}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992135613&doi=10.1109%2fISCAIE.2016.7575036&partnerID=40&md5=55c6d166768ed5fa3b504a2bd3441829}, doi = {10.1109/ISCAIE.2016.7575036}, isbn = {9781509015436}, year = {2016}, date = {2016-01-01}, journal = {ISCAIE 2016 - 2016 IEEE Symposium on Computer Applications and Industrial Electronics}, pages = {52-56}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {In this study, we deemed further to evaluate the performance of Neural Network (NN) and Support Vector Machine (SVM) in classifying the gait patterns between autism and normal children. Firstly, temporal spatial, kinetic and kinematic gait parameters of forty four subjects namely thirty two normal subjects and twelve autism children are acquired. Next, these three category gait parameters acted as inputs to both classifiers. Results showed that fusion of temporal spatial and kinematic contributed the highest accuracy rate for NN classifier specifically 95% whilst SVM with polynomial as kernel, 95% accuracy rate is contributed by fusion of all gait parameters as inputs to the classifier. In addition, the classifiers performance is validated by computing both value of sensitivity and specificity. With SVM using polynomial as kernel, sensitivity attained is 100% indicated that the classifier's ability to perfectly discriminate normal subjects from autism subjects whilst 85% specificity showed that SVM is able to identify autism subjects as autism based on their gait patterns at 85% rate. © 2016 IEEE.}, note = {cited By 5}, keywords = {Accuracy Rate, Autism, Classification (of information), Diseases, Gait Analysis, Gait Parameters, Gait Pattern, Industrial Electronics, Kinematics, Neural Networks, NN Classifiers, Sensitivity and Specificity, Support Vector Machines, Three Categories}, pubstate = {published}, tppubtype = {conference} } In this study, we deemed further to evaluate the performance of Neural Network (NN) and Support Vector Machine (SVM) in classifying the gait patterns between autism and normal children. Firstly, temporal spatial, kinetic and kinematic gait parameters of forty four subjects namely thirty two normal subjects and twelve autism children are acquired. Next, these three category gait parameters acted as inputs to both classifiers. Results showed that fusion of temporal spatial and kinematic contributed the highest accuracy rate for NN classifier specifically 95% whilst SVM with polynomial as kernel, 95% accuracy rate is contributed by fusion of all gait parameters as inputs to the classifier. In addition, the classifiers performance is validated by computing both value of sensitivity and specificity. With SVM using polynomial as kernel, sensitivity attained is 100% indicated that the classifier's ability to perfectly discriminate normal subjects from autism subjects whilst 85% specificity showed that SVM is able to identify autism subjects as autism based on their gait patterns at 85% rate. © 2016 IEEE. |
2015 |
Khir, N H B M; Ismail, M; Jamil, N; Razak, F H A Can spatiotemporal gait analysis identify a child with Autistic Spectrum Disorder? Conference Institute of Electrical and Electronics Engineers Inc., 2015, ISBN: 9781479957651, (cited By 0). Abstract | Links | BibTeX | Tags: Autism, Autism Spectrum Disorders, Children with Autism, Critical Analysis, Diseases, Economic and Social Effects, Gait Analysis, Gait Pattern, Literature Reviews, Manufacture, Quantitative Study, Robotics, Spatiotemporal @conference{Khir2015115, title = {Can spatiotemporal gait analysis identify a child with Autistic Spectrum Disorder?}, author = {N H B M Khir and M Ismail and N Jamil and F H A Razak}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959505294&doi=10.1109%2fROMA.2014.7295872&partnerID=40&md5=dbaae7a86b78fa037d60f4b944ed2dc6}, doi = {10.1109/ROMA.2014.7295872}, isbn = {9781479957651}, year = {2015}, date = {2015-01-01}, journal = {2014 IEEE International Symposium on Robotics and Manufacturing Automation, IEEE-ROMA2014}, pages = {115-119}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {The aim of this study is to investigate the ability of spatiotemporal gait analysis to identify the Autistic Spectrum Disorder child (ASD). Even though the interest in gait analysis is becoming popular among researchers these days, yet very few quantitative studies are done on children with autism. Since motor development is not influenced by both social and linguistic development, it is believed to be a probable bio-marker of autism. The spatiotemporal gait pattern is being explored to understand the difference it may bring upon in the future. Six findings from previous researches are reviewed and analyzed to understand the crucial factor involves in this research. From the literature review and critical analysis done, spatiotemporal gait analysis may be used to identify the ASD child because the gait patterns of ASD child are discovered to be different from normal children. © 2014 IEEE.}, note = {cited By 0}, keywords = {Autism, Autism Spectrum Disorders, Children with Autism, Critical Analysis, Diseases, Economic and Social Effects, Gait Analysis, Gait Pattern, Literature Reviews, Manufacture, Quantitative Study, Robotics, Spatiotemporal}, pubstate = {published}, tppubtype = {conference} } The aim of this study is to investigate the ability of spatiotemporal gait analysis to identify the Autistic Spectrum Disorder child (ASD). Even though the interest in gait analysis is becoming popular among researchers these days, yet very few quantitative studies are done on children with autism. Since motor development is not influenced by both social and linguistic development, it is believed to be a probable bio-marker of autism. The spatiotemporal gait pattern is being explored to understand the difference it may bring upon in the future. Six findings from previous researches are reviewed and analyzed to understand the crucial factor involves in this research. From the literature review and critical analysis done, spatiotemporal gait analysis may be used to identify the ASD child because the gait patterns of ASD child are discovered to be different from normal children. © 2014 IEEE. |