@article{ISI:000523676900014,
title = {Feature Selection of Electromyography Signals for Autism Spectrum Disorder Children During Gait Using Mann-Whitney Test},
author = {Mohd M N Nor and R Jailani and N M Tahir},
doi = {10.11113/jt.v82.13928},
issn = {0127-9696},
year = {2020},
date = {2020-03-01},
journal = {JURNAL TEKNOLOGI},
volume = {82},
number = {2},
pages = {113-120},
publisher = {PENERBIT UTM PRESS},
address = {PENERBIT UTM PRESS, SKUDAI, JOHOR, 81310, MALAYSIA},
abstract = {Autism Spectrum Disorder is a lifelong neurodevelopmental impairment
that affects brain growth and individual functional capabilities that
associates with unusual movement and gait disturbance. The aim of this
study is to investigate the significant features of EMG signals for
lower limbs and arms muscle between Autism Spectrum Disorder (ASD) and
Typical Development (TD) Children during walking. In this study, 30 ASD
and 30 Typical Development (TD) children aged between 6 to 13 years old
were asked to walk on the walkway naturally. The Electromyography (EMG)
signals of Biceps Femoris (BF), Rectus Femoris (RF), Tibialis Anterior
(TA), Gastrocnemius (GAS), Biceps Brachii (BB) and Tricep Brachii (TB)
muscles of the ASD and TD children were recorded by using surface EMG
sensors. The BF muscle is located at the posterior compartment of the
thigh whereas the RF muscle located in the anterior compartment of the
thigh. On the other hand, the TA muscle originates within the anterior
compartment of the leg, and Gas muscle originates at the posterior
compartment of the calf. Meanwhile, the BB muscle is in the front of the
upper arm between shoulder and elbow, and TB muscle is a large muscle on
the back of the upper arm limb. The data consists of 42 features from 7
walking phases of 6 muscles during one gait cycle were obtained from the
data collection. Firstly, the data will be normalized to one gait cycle
to standardize the length of EMG signals used for all subjects. Then,
the feature selection method using Mann-Whitney Test is applied to find
the significant features to differentiate between ASD and TD children
from the EMG signals. Out of 42 features, 5 were found to be the most
significant features of EMG signals between ASD and TD children, there
are TA muscle at 30% of gait cycle, Gas muscle at 50% and 60% of gait
cycle, and BB muscle at 10% and 80% of gait cycle with significant
values of 0.017, 0.049, 0.034, 0.021 and 0.003, respectively. These
findings are useful to both clinicians and parents as the lower limbs
and arm muscles can be valuable therapeutic parameter for ASD children's
rehabilitation plan. The findings of this research also suggest that the
significant difference of EMG signals obtained can be a parameter to
differentiate between ASD and TD children.},
keywords = {Autism Spectrum Disorders, Electromyography, Gait, Gait Analysis},
pubstate = {published},
tppubtype = {article}
}

@article{Hasan201755,
title = {The analysis of three-dimensional ground reaction forces during gait in children with autism spectrum disorders},
author = {C Z C Hasan and R Jailani and N Md Tahir and S Ilias},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015640386&doi=10.1016%2fj.ridd.2017.02.015&partnerID=40&md5=d6a9839cda7f62bcce9bdcca33d3d33b},
doi = {10.1016/j.ridd.2017.02.015},
issn = {08914222},
year = {2017},
date = {2017-01-01},
journal = {Research in Developmental Disabilities},
volume = {66},
pages = {55-63},
publisher = {Elsevier Inc.},
abstract = {Minimal information is known about the three-dimensional (3D) ground reaction forces (GRF) on the gait patterns of individuals with autism spectrum disorders (ASD). The purpose of this study was to investigate whether the 3D GRF components differ significantly between children with ASD and the peer controls. 15 children with ASD and 25 typically developing (TD) children had participated in the study. Two force plates were used to measure the 3D GRF data during walking. Time-series parameterization techniques were employed to extract 17 discrete features from the 3D GRF waveforms. By using independent t-test and Mann-Whitney U test, significant differences (p < 0.05) between the ASD and TD groups were found for four GRF features. Children with ASD demonstrated higher maximum braking force, lower relative time to maximum braking force, and lower relative time to zero force during mid-stance. Children with ASD were also found to have reduced the second peak of vertical GRF in the terminal stance. These major findings suggest that children with ASD experience significant difficulties in supporting their body weight and endure gait instability during the stance phase. The findings of this research are useful to both clinicians and parents who wish to provide these children with appropriate treatments and rehabilitation programs. © 2017 Elsevier Ltd},
note = {cited By 8},
keywords = {Age Distribution, Article, Autism, Autism Spectrum Disorders, Biomechanical Phenomena, Biomechanics, Body Equilibrium, Body Height, Body Mass, Body Weight, Children, Clinical Article, Controlled Study, Disease Assessment, Female, Gait, Gait Analysis, Gait Disorder, Ground Reaction Forces, Human, Imaging, Leg Length, Malaysia, Male, Neurologic Examination, Pathophysiology, Physiology, Postural Balance, Procedures, Psychology, Statistics, Three-Dimensional, Three-Dimensional Imaging, Three-Dimentional Ground Reaction Force, Walking},
pubstate = {published},
tppubtype = {article}
}

@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}
}