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 |
Al-Hiyali, M I; Ishak, A J; Harun, H; Ahmad, S A; Sulaiman, Wan W A A review in modification food-intake behavior by brain stimulation: Excess weight cases Journal Article NeuroQuantology, 16 (12), pp. 86-97, 2018, ISSN: 13035150, (cited By 2). Abstract | Links | BibTeX | Tags: Amygdala, Anoxia, Article, Autism, Binge Eating Disorder, Body Mass, Body Weight, Brain Depth Stimulation, Depolarization, Dietary Intake, Drug Craving, Eating Disorder, Electric Current, Electroencephalogram, Electroencephalography, Energy Consumption, Energy Expenditure, Feeding Behavior, Food Intake, Functional Magnetic Resonance Imaging, Gender, Health Status, Homeostasis, Human, Hunger, Lifestyle, Nerve Cell Membrane Steady Potential, Nerve Excitability, Neurofeedback, Neuromodulation, Nutritional Assessment, Outcome Assessment, Questionnaires, Repetitive Transcranial Magnetic Stimulation, Signal Processing, Training, Transcranial Direct Current Stimulation, Transcranial Magnetic Stimulation, Underweight @article{Al-Hiyali201886, title = {A review in modification food-intake behavior by brain stimulation: Excess weight cases}, author = {M I Al-Hiyali and A J Ishak and H Harun and S A Ahmad and W A Wan Sulaiman}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062843670&doi=10.14704%2fnq.2018.16.12.1894&partnerID=40&md5=235f66cef05a144be23472641f70bd1d}, doi = {10.14704/nq.2018.16.12.1894}, issn = {13035150}, year = {2018}, date = {2018-01-01}, journal = {NeuroQuantology}, volume = {16}, number = {12}, pages = {86-97}, publisher = {Anka Publishers}, abstract = {Obesity and overweight are frequently prescribed for dysfunction in food-intake behavior. Due to the widely prevalence of obesity in last year’s, there is demand for more studies which are aimed to modify the food-intake behavior. For the past decades many researches has applied in modify food-intake by brain training or stimulation. This review for neuroscience studies in modifying food-intake behavior, it’s involved three sections; The first section explained the role of brain activity in food-intake regulation, general ideas about biomedical devices in food-intake behavior are discussed in second section and third section focused on brain-stimulation systems. Finally, this paper concluded with main points that need to be taken into account when designing experimental study for modification food-intake behavior by brain stimulation according to previous studies recommendation and challenges. © 2018, Anka Publishers. All Rights Reserved.}, note = {cited By 2}, keywords = {Amygdala, Anoxia, Article, Autism, Binge Eating Disorder, Body Mass, Body Weight, Brain Depth Stimulation, Depolarization, Dietary Intake, Drug Craving, Eating Disorder, Electric Current, Electroencephalogram, Electroencephalography, Energy Consumption, Energy Expenditure, Feeding Behavior, Food Intake, Functional Magnetic Resonance Imaging, Gender, Health Status, Homeostasis, Human, Hunger, Lifestyle, Nerve Cell Membrane Steady Potential, Nerve Excitability, Neurofeedback, Neuromodulation, Nutritional Assessment, Outcome Assessment, Questionnaires, Repetitive Transcranial Magnetic Stimulation, Signal Processing, Training, Transcranial Direct Current Stimulation, Transcranial Magnetic Stimulation, Underweight}, pubstate = {published}, tppubtype = {article} } Obesity and overweight are frequently prescribed for dysfunction in food-intake behavior. Due to the widely prevalence of obesity in last year’s, there is demand for more studies which are aimed to modify the food-intake behavior. For the past decades many researches has applied in modify food-intake by brain training or stimulation. This review for neuroscience studies in modifying food-intake behavior, it’s involved three sections; The first section explained the role of brain activity in food-intake regulation, general ideas about biomedical devices in food-intake behavior are discussed in second section and third section focused on brain-stimulation systems. Finally, this paper concluded with main points that need to be taken into account when designing experimental study for modification food-intake behavior by brain stimulation according to previous studies recommendation and challenges. © 2018, Anka Publishers. All Rights Reserved. |
Hariharan, M; Sindhu, R; Vijean, V; Yazid, H; Nadarajaw, T; Yaacob, S; Polat, K Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification Journal Article Computer Methods and Programs in Biomedicine, 155 , pp. 39-51, 2018, ISSN: 01692607, (cited By 21). Abstract | Links | BibTeX | Tags: Accidents, Algorithms, Article, Artificial Neural Network, Asphyxia, Binary Dragonfly Optimization Aalgorithm, Classification (of information), Classification Algorithm, Classifier, Coding, Computer-Assisted, Constants and Coefficients, Crying, Database Systems, Databases, Deafness, Diagnosis, Energy, Entropy, Extraction, Extreme Learning Machine, Factual, Factual Database, Feature Extraction, Feature Selection Methods, Fuzzy System, Hearing Impairment, Human, Hunger, Infant, Infant Cry, Infant Cry Classifications, Jaundice, Kernel Method, Learning, Linear Predictive Coding, Machine Learning, Mathematical Transformations, Mel Frequency Cepstral Coefficient, Mel Frequency Cepstral Coefficients, Multi-Class Classification, Neural Networks, Nonlinear Dynamics, Nonlinear System, Optimization, Pain, Pathophysiology, Prematurity, Reproducibility, Reproducibility of Results, Signal Processing, Speech Recognition, Wavelet Analysis, Wavelet Packet, Wavelet Packet Transforms @article{Hariharan201839, title = {Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification}, author = {M Hariharan and R Sindhu and V Vijean and H Yazid and T Nadarajaw and S Yaacob and K Polat}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036611215&doi=10.1016%2fj.cmpb.2017.11.021&partnerID=40&md5=1f3b17817b00f07cadad6eb61c0f4bf9}, doi = {10.1016/j.cmpb.2017.11.021}, issn = {01692607}, year = {2018}, date = {2018-01-01}, journal = {Computer Methods and Programs in Biomedicine}, volume = {155}, pages = {39-51}, publisher = {Elsevier Ireland Ltd}, abstract = {Background and objective Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Methods Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Results Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. Conclusion The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals. © 2017 Elsevier B.V.}, note = {cited By 21}, keywords = {Accidents, Algorithms, Article, Artificial Neural Network, Asphyxia, Binary Dragonfly Optimization Aalgorithm, Classification (of information), Classification Algorithm, Classifier, Coding, Computer-Assisted, Constants and Coefficients, Crying, Database Systems, Databases, Deafness, Diagnosis, Energy, Entropy, Extraction, Extreme Learning Machine, Factual, Factual Database, Feature Extraction, Feature Selection Methods, Fuzzy System, Hearing Impairment, Human, Hunger, Infant, Infant Cry, Infant Cry Classifications, Jaundice, Kernel Method, Learning, Linear Predictive Coding, Machine Learning, Mathematical Transformations, Mel Frequency Cepstral Coefficient, Mel Frequency Cepstral Coefficients, Multi-Class Classification, Neural Networks, Nonlinear Dynamics, Nonlinear System, Optimization, Pain, Pathophysiology, Prematurity, Reproducibility, Reproducibility of Results, Signal Processing, Speech Recognition, Wavelet Analysis, Wavelet Packet, Wavelet Packet Transforms}, pubstate = {published}, tppubtype = {article} } Background and objective Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Methods Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Results Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. Conclusion The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals. © 2017 Elsevier B.V. |
2014 |
Sudirman, R; Hussin, S S Sensory responses of autism via electroencephalography for Sensory Profile Conference Institute of Electrical and Electronics Engineers Inc., 2014, ISBN: 9781479956869, (cited By 3). Abstract | Links | BibTeX | Tags: Autism, Discrete Wavelet Transforms, Diseases, Electroencephalography, Electrophysiology, Independent Component Analysis, International System, Learning, Sensory Analysis, Sensory Profiles, Sensory Profiling, Sensory Stimulation, Signal Processing, Standard Deviation, Wavelet Packet Transforms @conference{Sudirman2014626, title = {Sensory responses of autism via electroencephalography for Sensory Profile}, author = {R Sudirman and S S Hussin}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946435600&doi=10.1109%2fICCSCE.2014.7072794&partnerID=40&md5=3e6f1cfe19eae4fad359d2493aebd7e0}, doi = {10.1109/ICCSCE.2014.7072794}, isbn = {9781479956869}, year = {2014}, date = {2014-01-01}, journal = {Proceedings - 4th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2014}, pages = {626-631}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {The aim of this study is to investigate the brain signals of autism children through electroencephalography (EEG) associated to physical tasks. The physical task was meant to stimulate the sensitivity correlation of sensory response of a child. A group of autism children was chosen for this study and were given by five sensory stimulations which are audio, taste, touch, visual and vestibular. The acquisition of brain signals was acquainted using EEG Neurofax 9200 and the electrode positions were using 10-20 International System placements. The preprocessing signals were analyzed using independent component analysis (ICA) using EEGLAB Software and Discrete Wavelet Transform (DWT). The alpha wave was selected by level 6 decomposition and the extracted features represents the characteristic of the sensory task. The means, standard deviations and approximation entropy were extracted on the clean signals and forms into Sensory Profile (Sensory Profiling). From the overall results, the behavior of each autism children has been observed unstable emotion while running the sensory stimulation. The observation also helps to improve their learning strategy for the future work in assessment. © 2014 IEEE.}, note = {cited By 3}, keywords = {Autism, Discrete Wavelet Transforms, Diseases, Electroencephalography, Electrophysiology, Independent Component Analysis, International System, Learning, Sensory Analysis, Sensory Profiles, Sensory Profiling, Sensory Stimulation, Signal Processing, Standard Deviation, Wavelet Packet Transforms}, pubstate = {published}, tppubtype = {conference} } The aim of this study is to investigate the brain signals of autism children through electroencephalography (EEG) associated to physical tasks. The physical task was meant to stimulate the sensitivity correlation of sensory response of a child. A group of autism children was chosen for this study and were given by five sensory stimulations which are audio, taste, touch, visual and vestibular. The acquisition of brain signals was acquainted using EEG Neurofax 9200 and the electrode positions were using 10-20 International System placements. The preprocessing signals were analyzed using independent component analysis (ICA) using EEGLAB Software and Discrete Wavelet Transform (DWT). The alpha wave was selected by level 6 decomposition and the extracted features represents the characteristic of the sensory task. The means, standard deviations and approximation entropy were extracted on the clean signals and forms into Sensory Profile (Sensory Profiling). From the overall results, the behavior of each autism children has been observed unstable emotion while running the sensory stimulation. The observation also helps to improve their learning strategy for the future work in assessment. © 2014 IEEE. |
Bhat, S; Acharya, U R; Adeli, H; Bairy, G M; Adeli, A Automated diagnosis of autism: In search of a mathematical marker Journal Article Reviews in the Neurosciences, 25 (6), pp. 851-861, 2014, ISSN: 03341763, (cited By 34). Abstract | Links | BibTeX | Tags: Algorithms, Article, Autism, Autism Spectrum Disorders, Automation, Biological Model, Brain, Chaos Theory, Correlation Analysis, Detrended Fluctuation Analysis, Disease Marker, Electrode, Electroencephalogram, Electroencephalography, Entropy, Fourier Transformation, Fractal Analysis, Frequency Domain Analysis, Human, Mathematical Analysis, Mathematical Marker, Mathematical Parameters, Models, Neurologic Disease, Neurological, Nonlinear Dynamics, Nonlinear System, Pathophysiology, Priority Journal, Procedures, Signal Processing, Statistical Model, Time, Time Frequency Analysis, Wavelet Analysis @article{Bhat2014851, title = {Automated diagnosis of autism: In search of a mathematical marker}, author = {S Bhat and U R Acharya and H Adeli and G M Bairy and A Adeli}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925286949&doi=10.1515%2frevneuro-2014-0036&partnerID=40&md5=04858a5c9860e9027e3113835ca2e11f}, doi = {10.1515/revneuro-2014-0036}, issn = {03341763}, year = {2014}, date = {2014-01-01}, journal = {Reviews in the Neurosciences}, volume = {25}, number = {6}, pages = {851-861}, publisher = {Walter de Gruyter GmbH}, abstract = {Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder. © 2014 Walter de Gruyter GmbH.}, note = {cited By 34}, keywords = {Algorithms, Article, Autism, Autism Spectrum Disorders, Automation, Biological Model, Brain, Chaos Theory, Correlation Analysis, Detrended Fluctuation Analysis, Disease Marker, Electrode, Electroencephalogram, Electroencephalography, Entropy, Fourier Transformation, Fractal Analysis, Frequency Domain Analysis, Human, Mathematical Analysis, Mathematical Marker, Mathematical Parameters, Models, Neurologic Disease, Neurological, Nonlinear Dynamics, Nonlinear System, Pathophysiology, Priority Journal, Procedures, Signal Processing, Statistical Model, Time, Time Frequency Analysis, Wavelet Analysis}, pubstate = {published}, tppubtype = {article} } Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder. © 2014 Walter de Gruyter GmbH. |
2013 |
Selvaraj, J; Murugappan, M; Wan, K; Yaacob, S Classification of emotional states from electrocardiogram signals: A non-linear approach based on hurst Journal Article BioMedical Engineering Online, 12 (1), 2013, ISSN: 1475925X, (cited By 42). Abstract | Links | BibTeX | Tags: Adolescent, Adult, Aged, Article, Audio-Visual Stimulus, Autonomous Nervous Systems, Children, Classification Accuracy, Computer Based Training, Computer-Assisted, Electrocardiogram Signal, Electrocardiography, Emotion, Female, Fuzzy K-nearest Neighbor, Higher-Order Statistic (HOS), Human, Intellectual Disability, Interactive Computer Systems, Methodology, Middle Aged, Nonlinear Dynamics, Nonlinear System, Procedures, Real Time Systems, Signal Processing, Statistics, Young Adult @article{Selvaraj2013, title = {Classification of emotional states from electrocardiogram signals: A non-linear approach based on hurst}, author = {J Selvaraj and M Murugappan and K Wan and S Yaacob}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879017985&doi=10.1186%2f1475-925X-12-44&partnerID=40&md5=18c5309ac9f3017f455480f1ff732a30}, doi = {10.1186/1475-925X-12-44}, issn = {1475925X}, year = {2013}, date = {2013-01-01}, journal = {BioMedical Engineering Online}, volume = {12}, number = {1}, publisher = {BioMed Central Ltd.}, abstract = {Background: Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.Methods: Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature 'Hurst' was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers - Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm.Results: Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively.Conclusions: The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system. © 2013 Selvaraj et al.; licensee BioMed Central Ltd.}, note = {cited By 42}, keywords = {Adolescent, Adult, Aged, Article, Audio-Visual Stimulus, Autonomous Nervous Systems, Children, Classification Accuracy, Computer Based Training, Computer-Assisted, Electrocardiogram Signal, Electrocardiography, Emotion, Female, Fuzzy K-nearest Neighbor, Higher-Order Statistic (HOS), Human, Intellectual Disability, Interactive Computer Systems, Methodology, Middle Aged, Nonlinear Dynamics, Nonlinear System, Procedures, Real Time Systems, Signal Processing, Statistics, Young Adult}, pubstate = {published}, tppubtype = {article} } Background: Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.Methods: Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature 'Hurst' was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers - Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm.Results: Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively.Conclusions: The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system. © 2013 Selvaraj et al.; licensee BioMed Central Ltd. |
2012 |
Shamsuddin, S; Yussof, H; Ismail, L; Hanapiah, F A; Mohamed, S; Piah, H A; Zahari, N I Initial response of autistic children in human-robot interaction therapy with humanoid robot NAO Conference 2012, ISBN: 9781467309615, (cited By 103). Abstract | Links | BibTeX | Tags: Anthropomorphic Robots, Autism Spectrum Disorders, Autistic Children, Children with Autism, Developmental Disorders, Diseases, Human Computer Interaction, Human Robot Interaction, Humanoid Robot, Man Machine Systems, Pilot Experiment, Rehabilitation Robotics, Research, Robotics, Signal Processing, Visual Systems @conference{Shamsuddin2012188, title = {Initial response of autistic children in human-robot interaction therapy with humanoid robot NAO}, author = {S Shamsuddin and H Yussof and L Ismail and F A Hanapiah and S Mohamed and H A Piah and N I Zahari}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861537641&doi=10.1109%2fCSPA.2012.6194716&partnerID=40&md5=32572eb3ebc7d201c02a90908128ae28}, doi = {10.1109/CSPA.2012.6194716}, isbn = {9781467309615}, year = {2012}, date = {2012-01-01}, journal = {Proceedings - 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, CSPA 2012}, pages = {188-193}, abstract = {The overall context proposed in this paper is part of our long-standing goal to contribute to a group of community that suffers from Autism Spectrum Disorder (ASD); a lifelong developmental disability. The objective of this paper is to present the development of our pilot experiment protocol where children with ASD will be exposed to the humanoid robot NAO. This fully programmable humanoid offers an ideal research platform for human-robot interaction (HRI). This study serves as the platform for fundamental investigation to observe the initial response and behavior of the children in the said environment. The system utilizes external cameras, besides the robot's own visual system. Anticipated results are the real initial response and reaction of ASD children during the HRI with the humanoid robot. This shall leads to adaptation of new procedures in ASD therapy based on HRI, especially for a non-technical-expert person to be involved in the robotics intervention during the therapy session. © 2012 IEEE.}, note = {cited By 103}, keywords = {Anthropomorphic Robots, Autism Spectrum Disorders, Autistic Children, Children with Autism, Developmental Disorders, Diseases, Human Computer Interaction, Human Robot Interaction, Humanoid Robot, Man Machine Systems, Pilot Experiment, Rehabilitation Robotics, Research, Robotics, Signal Processing, Visual Systems}, pubstate = {published}, tppubtype = {conference} } The overall context proposed in this paper is part of our long-standing goal to contribute to a group of community that suffers from Autism Spectrum Disorder (ASD); a lifelong developmental disability. The objective of this paper is to present the development of our pilot experiment protocol where children with ASD will be exposed to the humanoid robot NAO. This fully programmable humanoid offers an ideal research platform for human-robot interaction (HRI). This study serves as the platform for fundamental investigation to observe the initial response and behavior of the children in the said environment. The system utilizes external cameras, besides the robot's own visual system. Anticipated results are the real initial response and reaction of ASD children during the HRI with the humanoid robot. This shall leads to adaptation of new procedures in ASD therapy based on HRI, especially for a non-technical-expert person to be involved in the robotics intervention during the therapy session. © 2012 IEEE. |