@article{Hasan201774,
title = {Use of statistical approaches and artificial neural networks to identify gait deviations in children with autism spectrum disorder},
author = {C Z C Hasan and R Jailani and N Md Tahir},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043500605&partnerID=40&md5=6f2ffe7c2f5daf9fd02d4456acb94438},
issn = {19984510},
year = {2017},
date = {2017-01-01},
journal = {International Journal of Biology and Biomedical Engineering},
volume = {11},
pages = {74-79},
publisher = {North Atlantic University Union NAUN},
abstract = {Automated differentiation of ASD gait from normal gait patterns is important for early diagnosis as well as ensuring rapid quantitative clinical decision and appropriate treatment planning. This study explores the use of statistical feature selection approaches and artificial neural networks (ANN) for automated identification of gait deviations in children with ASD, on the basis of dominant gait features derived from the three-dimensional (3D) joint kinematic data. The gait data from 30 ASD children and 30 normal healthy children were measured using a state-of-the-art 3D motion analysis system during self-selected speed barefoot walking. Kinematic gait features from the sagittal, frontal and transverse joint angles waveforms at the pelvis, hip, knee, and ankle were extracted using time-series parameterization. Two statistical feature selection techniques, namely the between-group tests (independent samples t-test and Mann-Whitney U test) and the stepwise discriminant analysis (SWDA) were adopted as feature selector to select the meaningful gait features that were then used to train the ANN. The 10-fold cross-validation test results indicate that the selected gait features using SWDA technique are more reliable for ASD gait classification with 91.7% accuracy, 93.3% sensitivity, and 90.0% specificity. The findings of the current study demonstrate that kinematic gait features with the combination of SWDA feature selector and ANN classifier would serve as a potential tool for early diagnosis of gait deviations in children with ASD as well as provide support to clinicians and therapists for making objective, accurate, and rapid clinical decisions that lead to the appropriate targeted treatments. © 2017 North Atlantic University Union NAUN. All Rights Reserved.},
note = {cited By 1},
keywords = {Article, Artificial Neural Network, Autism, Body Height, Body Mass, Children, Clinical Article, Controlled Study, Discriminant Analysis, Early Diagnosis, Female, Gait, Gait Analysis, Gait Disorder, Human, Learning, Male, Pediatrics, School Child, Statistical Analysis, Statistics, Time Series Analysis},
pubstate = {published},
tppubtype = {article}
}