@conference{Ilias2017275,
title = {Feature extraction of autism gait data using principal component analysis and linear discriminant analysis},
author = {S Ilias and N M Tahir and R Jailani},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034081031&doi=10.1109%2fIEACON.2016.8067391&partnerID=40&md5=7deaef6538413df7bfaf7cf723001d72},
doi = {10.1109/IEACON.2016.8067391},
isbn = {9781509009251},
year = {2017},
date = {2017-01-01},
journal = {IEACon 2016 - 2016 IEEE Industrial Electronics and Applications Conference},
pages = {275-279},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In this research, the application of machine learning approach specifically support vector machine along with principal component analysis and linear discriminant analysis as feature extractions are evaluated and validated in discriminating gait features between normal subjects and autism children. Gait features of 32 normal and 12 autism children were recorded and analyzed using VICON motion analysis system and a force platform during normal walking. Here, twenty one gait features describing the three types of gait characteristics namely basic, kinetic and kinematic in these children are extracted. Further, with these gait features as input during classification, the ability of SVM as classifier are investigated using three different kernel functions specifically linear, polynomial, and radial basis. Results showed that LDA as feature extraction is the highest accuracy with kinematic parameters as gait features along with polynomial function as kernel for the SVM classifier. This finding proven that LDA is suitable as feature extraction and SVM is indeed apt as gait classifier in classifying the gait pattern autism and normal children. © 2016 IEEE.},
note = {cited By 0},
keywords = {Classification (of information), Discriminant Analysis, Diseases, Extraction, Feature Extraction, Gait Analysis, Gait Classification, Image Retrieval, Industrial Electronics, Kernel Function, Kinematic Parameters, Kinematics, Learning, Linear Discriminant Analysis, Machine Learning Approaches, Motion Analysis System, Polynomial Functions, Principal Component Analysis, Support Vector Machines, SVM Classifiers},
pubstate = {published},
tppubtype = {conference}
}

@conference{Nor2016226,
title = {Analysis of EMG signals of TA and GAS muscles during walking of Autism Spectrum Disorder (ASD) children},
author = {M N M Nor and R Jailani and N M Tahir},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992153602&doi=10.1109%2fISCAIE.2016.7575068&partnerID=40&md5=7aaa147660a67bf4c2ddaa31f0e78717},
doi = {10.1109/ISCAIE.2016.7575068},
isbn = {9781509015436},
year = {2016},
date = {2016-01-01},
journal = {ISCAIE 2016 - 2016 IEEE Symposium on Computer Applications and Industrial Electronics},
pages = {226-230},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This paper presents an analysis of Electromyography (EMG) signals of lower limb muscles during walking among children. Total of 18 children consists of 8 Autism Spectrum Disorder (ASD) children and 10 Typical Development (TD) children aged between 6 to 13 years old were participated in this study. The muscles of Tibialis Anterior (TA) and Gastrocnemius (GAS) were examined and the EMG signals data were obtained using Trigno Wireless EMG System at Human Motion and Analysis Laboratory, UiTM Shah Alam. The EMG signals patterns for TA and GAS muscles will be explained and the independent t-Test will be analyzed to investigate the differences of walking gait in ASD children and TD children. The result shows that there is significant differences at Gastrocnemius (GAS) muscle between ASD and TD children during midstance where p value is equal to 0.042. From this study, the EMG signal for GAS muscle play an important role in differentiate between ASD and TD children. © 2016 IEEE.},
note = {cited By 4},
keywords = {ASD Children, Autism Spectrum Disorders, Diseases, Electromyography, Gases, Gastrocnemius, Human Motions, Industrial Electronics, Muscle, Tibialis Anterior, Typical Development, Walking Gait, Walking Pattern},
pubstate = {published},
tppubtype = {conference}
}

@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}
}

@conference{KhazaalShams2011653,
title = {Characterizing autistic disorder based on principle component analysis},
author = {W Khazaal Shams and A W Abdul Rahman},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855644760&doi=10.1109%2fISIEA.2011.6108797&partnerID=40&md5=c486566e2d7ff404d830704c0b404067},
doi = {10.1109/ISIEA.2011.6108797},
isbn = {9781457714184},
year = {2011},
date = {2011-01-01},
journal = {2011 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2011},
pages = {653-657},
abstract = {Autism is often diagnosed during preschool or toddled age. This diagnosis often depends on behavioral test. It is known that individuals with autism have abnormal brain signals different from typical persons yet this difference in signals is slight that it is often difficult to distinguish from the normal. However, Electroencephalogram (EEG) signals have a lot of information which reflect the behavior of brain functions which therefore captures the marker for autism, help to early diagnose and speed the treatment. This work investigates and compares classification process for autism in open-eyed tasks and motor movement by using Principle Component Analysis (PCA) for feature extracted in Time-frequency domain to reduce data dimension. The results show that the proposed method gives accuracy in the range 90-100% for autism and normal children in motor task and around 90% to detect normal in open-eyed tasks though difficult to detect autism in this task. © 2011 IEEE.},
note = {cited By 6},
keywords = {Autism, Brain Function, Brain Signals, Classification Process, Data Dimensions, Diseases, Electroencephalogram Signals, Electroencephalography, Frequency Domain Analysis, Industrial Electronics, Motor Movements, Motor Tasks, PCA, Principal Component Analysis, Signal Detection, Time Frequency Domain},
pubstate = {published},
tppubtype = {conference}
}

@conference{Sudirman2010401,
title = {Study of electroencephalography signal of autism and down syndrome children using FFT},
author = {Sudirman and S Saidin and N Mat Safri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79251542066&doi=10.1109%2fISIEA.2010.5679434&partnerID=40&md5=17fce4f69b27a3cc644f36c118b6ec6e},
doi = {10.1109/ISIEA.2010.5679434},
isbn = {9781424476473},
year = {2010},
date = {2010-01-01},
journal = {ISIEA 2010 - 2010 IEEE Symposium on Industrial Electronics and Applications},
pages = {401-406},
abstract = {Electroencephalography (EEG) signal between normal and special children is slightly different. Different types of special children will generate different shape of EEG patterns depend on their neurological function. This paper demonstrates the classification of EEG signal for special children: to determine and to classify level and pattern of EEG signal for autism and Down syndrome children. EEG signal was recorded and captured from normal and special children based on their visual response using Visual Evoked Potential (VEP) method. The data is analyzed using Fast Fourier Transform (FFT), so that, normal and special children can be distinguished based on alpha (α) value. As a result, alpha value for normal children at 10 Hz is higher than autism and Down syndrome children. A friendly user interface was built for easy storage and visualization. ©2010 IEEE.},
note = {cited By 15},
keywords = {Alpha Value, Autism, Down Syndrome, EEG Signals, Electroencephalography, Electrophysiology, Fast Fourier Transforms, Industrial Electronics, Metadata, User Interfaces, Visual Evoked Potential, Visualization},
pubstate = {published},
tppubtype = {conference}
}