@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}
}

@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}
}