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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2019 |
Hasan, C Z C; Jailani, R; Tahir, N M 2018-October , Institute of Electrical and Electronics Engineers Inc., 2019, ISSN: 21593442, (cited By 0). Abstract | Links | BibTeX | Tags: 10 Fold Cross Validation, 3D Modeling, Autism Spectrum Disorders, Biophysics, Clinical Decision Making, Computer Aided Diagnosis, Decision Making, Discriminant Analysis, Diseases, Gait Analysis, Gait Classification, Ground Reaction Forces, Neural Networks, Parameterization Techniques, Pattern Recognition, Petroleum Reservoir Evaluation, Program Diagnostics, Support Vector Machines, Targeted Treatment, Three-Dimensional @conference{Hasan20192436, title = {ANN and SVM Classifiers in Identifying Autism Spectrum Disorder Gait Based on Three-Dimensional Ground Reaction Forces}, author = {C Z C Hasan and R Jailani and N M Tahir}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063202256&doi=10.1109%2fTENCON.2018.8650468&partnerID=40&md5=c697d0c43ebd77d76d74cb3726872f42}, doi = {10.1109/TENCON.2018.8650468}, issn = {21593442}, year = {2019}, date = {2019-01-01}, journal = {IEEE Region 10 Annual International Conference, Proceedings/TENCON}, volume = {2018-October}, pages = {2436-2440}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {Autism spectrum disorder (ASD) is a complex and lifelong neurodevelopmental condition that occurs in early childhood and is associated with unusual movement and gait disturbances. An automated and accurate recognition of ASD gait provides assistance in diagnosis and clinical decision-making as well as improving targeted treatment. This paper explores the use of two well-known machine learning classifiers, artificial neural network (ANN) and support vector machine (SVM) in distinguishing ASD and normal gait patterns based on prominent gait features derived from three-dimensional (3D) ground reaction forces (GRFs) data. The 3D GRFs data of 30 children with ASD and 30 typically developing children were obtained using two force plates during self-determined speed of barefoot walking. Time-series parameterization techniques were applied to the 3D GRFs waveforms to extract the significant gait features. The stepwise method of discriminant analysis (SWDA) was employed to determine the dominant GRF gait features in order to classify ASD and typically developing groups. The 10-fold cross-validation test results indicate that the ANN model with three dominant GRF input features outperformed the kernel-based SVM models with 93.3% accuracy, 96.7% sensitivity, and 90.0% specificity. The findings of this study demonstrate the reliability of using the 3D GRF input features, in combination with SWDA feature selection and ANN classification model as an appropriate method that may be beneficial for the diagnosis of ASD gait as well as for evaluation purpose of the treatment programs. © 2018 IEEE.}, note = {cited By 0}, keywords = {10 Fold Cross Validation, 3D Modeling, Autism Spectrum Disorders, Biophysics, Clinical Decision Making, Computer Aided Diagnosis, Decision Making, Discriminant Analysis, Diseases, Gait Analysis, Gait Classification, Ground Reaction Forces, Neural Networks, Parameterization Techniques, Pattern Recognition, Petroleum Reservoir Evaluation, Program Diagnostics, Support Vector Machines, Targeted Treatment, Three-Dimensional}, pubstate = {published}, tppubtype = {conference} } Autism spectrum disorder (ASD) is a complex and lifelong neurodevelopmental condition that occurs in early childhood and is associated with unusual movement and gait disturbances. An automated and accurate recognition of ASD gait provides assistance in diagnosis and clinical decision-making as well as improving targeted treatment. This paper explores the use of two well-known machine learning classifiers, artificial neural network (ANN) and support vector machine (SVM) in distinguishing ASD and normal gait patterns based on prominent gait features derived from three-dimensional (3D) ground reaction forces (GRFs) data. The 3D GRFs data of 30 children with ASD and 30 typically developing children were obtained using two force plates during self-determined speed of barefoot walking. Time-series parameterization techniques were applied to the 3D GRFs waveforms to extract the significant gait features. The stepwise method of discriminant analysis (SWDA) was employed to determine the dominant GRF gait features in order to classify ASD and typically developing groups. The 10-fold cross-validation test results indicate that the ANN model with three dominant GRF input features outperformed the kernel-based SVM models with 93.3% accuracy, 96.7% sensitivity, and 90.0% specificity. The findings of this study demonstrate the reliability of using the 3D GRF input features, in combination with SWDA feature selection and ANN classification model as an appropriate method that may be beneficial for the diagnosis of ASD gait as well as for evaluation purpose of the treatment programs. © 2018 IEEE. |
2013 |
Hashim, H; Yussof, H; Hanapiah, F A; Shamsuddin, S; Ismail, L; Malik, N A Robot-assisted to elicit behaviors for autism screening Journal Article Applied Mechanics and Materials, 393 , pp. 567-572, 2013, ISSN: 16609336, (cited By 2). Abstract | Links | BibTeX | Tags: Anthropomorphic Robots, Autism Spectrum Disorders, Diseases, Early Intervention, Humanoid Robot, Humanoid Robot NAO, Individual Behaviour, Intervention, Mechanical Engineering, Program Diagnostics, Quantitative Measurement, Robotics, Screening Process @article{Hashim2013567, title = {Robot-assisted to elicit behaviors for autism screening}, author = {H Hashim and H Yussof and F A Hanapiah and S Shamsuddin and L Ismail and N A Malik}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886257860&doi=10.4028%2fwww.scientific.net%2fAMM.393.567&partnerID=40&md5=9ef0b91be1f79ae1771901b04e271636}, doi = {10.4028/www.scientific.net/AMM.393.567}, issn = {16609336}, year = {2013}, date = {2013-01-01}, journal = {Applied Mechanics and Materials}, volume = {393}, pages = {567-572}, abstract = {Early screening and diagnosis of Autism spectrums is essential to determine the best means of early intervention program. Since there is no sign in biological for autism, screening and assessment must focus on the behavioral deficits. Somehow screening is not a diagnosis, but a filter that picks out children for subsequent assessment. The aim of this paper is to propose and to ignite discussion concerning robotic assisted in autism screening process to enable early diagnosis and intervention. This process combines (a) selection of an autism screening tool (b) refinement of screening subscales and (c) integration of subscales with robot action. We use Gilliam Autism Rating Scale-2 (GARS-2) inversely integrated with humanoid robot Nao to produce a counter action to elicit individual behaviours for screening and diagnosis purposes. In extracting of GARS-2, we had considered the limitation and sensitivity when a robot tries to assist in the process of screening and diagnosis. Integrating robotics into innovative treatments however highlighted the need for additional rigorous empirical studies with quantitative measurement. © (2013) Trans Tech Publications, Switzerland.}, note = {cited By 2}, keywords = {Anthropomorphic Robots, Autism Spectrum Disorders, Diseases, Early Intervention, Humanoid Robot, Humanoid Robot NAO, Individual Behaviour, Intervention, Mechanical Engineering, Program Diagnostics, Quantitative Measurement, Robotics, Screening Process}, pubstate = {published}, tppubtype = {article} } Early screening and diagnosis of Autism spectrums is essential to determine the best means of early intervention program. Since there is no sign in biological for autism, screening and assessment must focus on the behavioral deficits. Somehow screening is not a diagnosis, but a filter that picks out children for subsequent assessment. The aim of this paper is to propose and to ignite discussion concerning robotic assisted in autism screening process to enable early diagnosis and intervention. This process combines (a) selection of an autism screening tool (b) refinement of screening subscales and (c) integration of subscales with robot action. We use Gilliam Autism Rating Scale-2 (GARS-2) inversely integrated with humanoid robot Nao to produce a counter action to elicit individual behaviours for screening and diagnosis purposes. In extracting of GARS-2, we had considered the limitation and sensitivity when a robot tries to assist in the process of screening and diagnosis. Integrating robotics into innovative treatments however highlighted the need for additional rigorous empirical studies with quantitative measurement. © (2013) Trans Tech Publications, Switzerland. |