Gender classification based on gait features for healthy children / Nur Khalidah Zakaria

This thesis describes a representation of gait analysis for the purpose of gender classification of healthy children using the most significant gait features. In this study, the gait parameters were measured using 3D motion analysis system. In order to obtain the most significant gait features, nume...

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Main Author:	Zakaria, Nur Khalidah
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Computer engineering Computer hardware
Online Access:	https://ir.uitm.edu.my/id/eprint/98985/1/98985.pdf
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id	my-uitm-ir.98985
record_format	uketd_dc
spelling	my-uitm-ir.989852024-12-16T03:41:41Z Gender classification based on gait features for healthy children / Nur Khalidah Zakaria 2016 Zakaria, Nur Khalidah Computer engineering. Computer hardware This thesis describes a representation of gait analysis for the purpose of gender classification of healthy children using the most significant gait features. In this study, the gait parameters were measured using 3D motion analysis system. In order to obtain the most significant gait features, numerical analysis using statistical method was performed. The three gait parameters which were spatiotemporal, kinematics and kinetics parameters were tested. From the result, it is noted that the most significant difference between genders only existed at kinematic parameter. Out of 36 gait features in kinematic parameter, only four were found to be the most significant gait features. These features were added into the ANN as an input to classify gender of healthy children. The ANN networks were optimized by adjusting the number of hidden neurons and thresholds. Since the size of the original gait features was too small due to small sample size of healthy children, the generation of synthetic data was performed based on the original gait features data. The result from the performance measures show that synthetic data obtained better accuracy compared to original data, since ANN performed better with large amount of data. The ANN network for this study is 4-6-1 with 0.3 thresholds. The performance measures show that this network achieved 76%, 87% and 91.3% of accuracy for training, validation and testing respectivel 2016 Thesis https://ir.uitm.edu.my/id/eprint/98985/ https://ir.uitm.edu.my/id/eprint/98985/1/98985.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Jailani, Rozita
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Jailani, Rozita
topic	Computer engineering Computer hardware
spellingShingle	Computer engineering Computer hardware Zakaria, Nur Khalidah Gender classification based on gait features for healthy children / Nur Khalidah Zakaria
description	This thesis describes a representation of gait analysis for the purpose of gender classification of healthy children using the most significant gait features. In this study, the gait parameters were measured using 3D motion analysis system. In order to obtain the most significant gait features, numerical analysis using statistical method was performed. The three gait parameters which were spatiotemporal, kinematics and kinetics parameters were tested. From the result, it is noted that the most significant difference between genders only existed at kinematic parameter. Out of 36 gait features in kinematic parameter, only four were found to be the most significant gait features. These features were added into the ANN as an input to classify gender of healthy children. The ANN networks were optimized by adjusting the number of hidden neurons and thresholds. Since the size of the original gait features was too small due to small sample size of healthy children, the generation of synthetic data was performed based on the original gait features data. The result from the performance measures show that synthetic data obtained better accuracy compared to original data, since ANN performed better with large amount of data. The ANN network for this study is 4-6-1 with 0.3 thresholds. The performance measures show that this network achieved 76%, 87% and 91.3% of accuracy for training, validation and testing respectivel
format	Thesis
qualification_level	Master's degree
author	Zakaria, Nur Khalidah
author_facet	Zakaria, Nur Khalidah
author_sort	Zakaria, Nur Khalidah
title	Gender classification based on gait features for healthy children / Nur Khalidah Zakaria
title_short	Gender classification based on gait features for healthy children / Nur Khalidah Zakaria
title_full	Gender classification based on gait features for healthy children / Nur Khalidah Zakaria
title_fullStr	Gender classification based on gait features for healthy children / Nur Khalidah Zakaria
title_full_unstemmed	Gender classification based on gait features for healthy children / Nur Khalidah Zakaria
title_sort	gender classification based on gait features for healthy children / nur khalidah zakaria
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2016
url	https://ir.uitm.edu.my/id/eprint/98985/1/98985.pdf
_version_	1818587998324785152

Gender classification based on gait features for healthy children / Nur Khalidah Zakaria

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