@conference{Ilias2017540,
title = {Development of three dimensional gait pattern in autism children - a review},
author = {S Ilias and N M Tahir and R Jailani},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019000981&doi=10.1109%2fICCSCE.2016.7893635&partnerID=40&md5=37aaf5f94b177ecfa164c432d32b5dfe},
doi = {10.1109/ICCSCE.2016.7893635},
isbn = {9781509011780},
year = {2017},
date = {2017-01-01},
journal = {Proceedings - 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016},
pages = {540-545},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Recently, gait patterns of children with autism is of interest in the gait community in order to identify significant gait parameter namely the three dimensional (3D) gait features such as spatial temporal, kinematic and kinetic. This is because gait pattern provides clinicians and researchers in understanding the trajectory of gait development. Understanding the characteristics and identifying gait pattern is essential in order to distinguish normal as well as abnormal gait pattern. Hence the purpose of this review is to identify deviations gait in children with autism based on criteria specifically subject character; measurement, type of gait variables measured; method of classification and major findings. Several gait variables from different instrumentation for gait analysis is reviewed too. Development of gait patterns via assessing gait deviations in children with ASD could assist clinician and researchers to differentiate gait pattern abnormality in diagnosing, clinical decision-making and treatment planning as well. © 2016 IEEE.},
note = {cited By 0},
keywords = {Abnormal Gait, Children with Autism, Clinical Decision Making, Control Systems, Decision Making, Diseases, Enzyme Kinetics, Gait Analysis, Gait Classification, Kinematics, Spatial Temporals, Temporal Spatial, Three-Dimensional, Three-Dimensional Computer Graphics, Treatment Planning},
pubstate = {published},
tppubtype = {conference}
}

@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{Ilias201767,
title = {Linear Discriminant Analysis in Classifying Walking Gait of Autistic Children},
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-s2.0-85048377850&doi=10.1109%2fEMS.2017.22&partnerID=40&md5=06de53be2b4f3976ddcc420067ab6e44},
doi = {10.1109/EMS.2017.22},
isbn = {9781538614099},
year = {2017},
date = {2017-01-01},
journal = {Proceedings - UKSim-AMSS 11th European Modelling Symposium on Computer Modelling and Simulation, EMS 2017},
pages = {67-72},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The aim of this research is to investigate the effectiveness between Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) along with neural network (NN) in classifying the gait of autistic children as compared to control group. Twelve autistic children and thirty two normal children participated in this study. Firstly the walking gait of these two groups are acquired using VICON Motion Analysis System to extract the three dimensional (3D) gait features that comprised of 21 gait features namely five features from basic temporal spatial, five features represented the kinetic parameters and twelve features from kinematic. Further, PCA and LDA are utilized as feature extraction in determining the significant features among these gait features. With NN as classifier, results showed that LDA as feature extraction outperform PCA for classification of autism versus normal children namely kinematic gait patterns attained 98.44% accuracy followed by basic temporal spatial gait features with accuracy of 87.5%. © 2017 IEEE.},
note = {cited By 0},
keywords = {Autism, Autistic Children, Children with Autism, Discriminant Analysis, Diseases, Extraction, Feature Extraction, Gait Analysis, Gait Classification, Kinematics, Linear Discriminant Analysis, Motion Analysis System, Neural Networks, Principal Component Analysis, Three-Dimensional},
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}
}