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Identification of autism spectrum disorder gait patterns based on three-dimensional gait analysis / Che Zawiyah Che Hasan

Autism spectrum disorder (ASD) is a complex and lifelong neurodevelopmental disorder that affects the brain growth, functional capabilities, and quality of life of an individual. The existence of movement and gait abnormalities particularly in children with ASD are presently regarded as additional e...

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Main Author: Che Hasan, Che Zawiyah
Format: Thesis
Language:English
Published: 2019
Online Access:https://ir.uitm.edu.my/id/eprint/83467/1/83467.pdf
https://ir.uitm.edu.my/id/eprint/83467/
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spelling my.uitm.ir.834672023-11-16T09:01:25Z https://ir.uitm.edu.my/id/eprint/83467/ Identification of autism spectrum disorder gait patterns based on three-dimensional gait analysis / Che Zawiyah Che Hasan Che Hasan, Che Zawiyah Autism spectrum disorder (ASD) is a complex and lifelong neurodevelopmental disorder that affects the brain growth, functional capabilities, and quality of life of an individual. The existence of movement and gait abnormalities particularly in children with ASD are presently regarded as additional evidence that supports the diagnosis of ASD. In clinical practice, most of the assessment of gait abnormalities are primarily grounded on subjective judgements made by experienced clinicians which are usually manually interpreted, time-consuming, burdensome, and often include subjective and inaccurate evaluations. Hence, automated identification of abnormalities in ASD gait patterns is important for early intervention and post-treatment monitoring. So far, however, there has been little discussion dealing specifically with automated identification of ASD gait patterns. Thus, this study endeavours to propose an automated machine learning-based approach for accurate identification of ASD and normal gait patterns on the basis of dominant gait features acquired from three dimensional (3D) gait analysis. The proposed approach consisted of five sequential stages of data acquisition, data processing, features extraction, features selection, and model classification. The gait data of 30 children with ASD and 30 healthy typically developing (TD) children were acquired using a state-of-the-art 3D motion capture system and two force plates during the self-selected speed of barefoot walking. Time-series parameterisation techniques were applied to the kinematic and kinetic waveforms to extract useful gait features. Two statistical feature selection techniques, namely the statistical hypothesis tests and the stepwise method of discriminant analysis were utilised to select dominant gait features that would best differentiate between ASD and TD gait patterns. Four different machine learning classifiers which include linear discriminant analysis (LDA), k-nearest neighbour (KNN), kernel-based support vector machines (SVMs), and artificial neural networks (ANN) were employed to perform the classification tasks. The superior classification performance was achieved using the ANN classifier with six dominant gait features. The 10-fold cross-validation test results showed that the proposed classification model was able to produce the optimum classification performance with 98.3% accuracy, 96.7% sensitivity, and 100.0% specificity. These findings suggest the potential use of the proposed methods as an aided tool that may be beneficial for clinicians to perform an automated and accurate diagnosis of ASD gait patterns as well as for evaluation purposes of the treatment programmes. 2019 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/83467/1/83467.pdf Identification of autism spectrum disorder gait patterns based on three-dimensional gait analysis / Che Zawiyah Che Hasan. (2019) PhD thesis, thesis, Universiti Teknologi MARA (UiTM).
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
description Autism spectrum disorder (ASD) is a complex and lifelong neurodevelopmental disorder that affects the brain growth, functional capabilities, and quality of life of an individual. The existence of movement and gait abnormalities particularly in children with ASD are presently regarded as additional evidence that supports the diagnosis of ASD. In clinical practice, most of the assessment of gait abnormalities are primarily grounded on subjective judgements made by experienced clinicians which are usually manually interpreted, time-consuming, burdensome, and often include subjective and inaccurate evaluations. Hence, automated identification of abnormalities in ASD gait patterns is important for early intervention and post-treatment monitoring. So far, however, there has been little discussion dealing specifically with automated identification of ASD gait patterns. Thus, this study endeavours to propose an automated machine learning-based approach for accurate identification of ASD and normal gait patterns on the basis of dominant gait features acquired from three dimensional (3D) gait analysis. The proposed approach consisted of five sequential stages of data acquisition, data processing, features extraction, features selection, and model classification. The gait data of 30 children with ASD and 30 healthy typically developing (TD) children were acquired using a state-of-the-art 3D motion capture system and two force plates during the self-selected speed of barefoot walking. Time-series parameterisation techniques were applied to the kinematic and kinetic waveforms to extract useful gait features. Two statistical feature selection techniques, namely the statistical hypothesis tests and the stepwise method of discriminant analysis were utilised to select dominant gait features that would best differentiate between ASD and TD gait patterns. Four different machine learning classifiers which include linear discriminant analysis (LDA), k-nearest neighbour (KNN), kernel-based support vector machines (SVMs), and artificial neural networks (ANN) were employed to perform the classification tasks. The superior classification performance was achieved using the ANN classifier with six dominant gait features. The 10-fold cross-validation test results showed that the proposed classification model was able to produce the optimum classification performance with 98.3% accuracy, 96.7% sensitivity, and 100.0% specificity. These findings suggest the potential use of the proposed methods as an aided tool that may be beneficial for clinicians to perform an automated and accurate diagnosis of ASD gait patterns as well as for evaluation purposes of the treatment programmes.
format Thesis
author Che Hasan, Che Zawiyah
spellingShingle Che Hasan, Che Zawiyah
Identification of autism spectrum disorder gait patterns based on three-dimensional gait analysis / Che Zawiyah Che Hasan
author_facet Che Hasan, Che Zawiyah
author_sort Che Hasan, Che Zawiyah
title Identification of autism spectrum disorder gait patterns based on three-dimensional gait analysis / Che Zawiyah Che Hasan
title_short Identification of autism spectrum disorder gait patterns based on three-dimensional gait analysis / Che Zawiyah Che Hasan
title_full Identification of autism spectrum disorder gait patterns based on three-dimensional gait analysis / Che Zawiyah Che Hasan
title_fullStr Identification of autism spectrum disorder gait patterns based on three-dimensional gait analysis / Che Zawiyah Che Hasan
title_full_unstemmed Identification of autism spectrum disorder gait patterns based on three-dimensional gait analysis / Che Zawiyah Che Hasan
title_sort identification of autism spectrum disorder gait patterns based on three-dimensional gait analysis / che zawiyah che hasan
publishDate 2019
url https://ir.uitm.edu.my/id/eprint/83467/1/83467.pdf
https://ir.uitm.edu.my/id/eprint/83467/
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score 13.211869

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