Parkinson disease gait classification based on machine learning approach / Hany Hazfiza Manap

The aim of this thesis is to develop a Parkinson gait recognition technique that is able to evaluate and distinguish gait deviations experienced by Parkinson Disease (PD) patients from normal pattern. The research can be divided into two phase namely gait analysis of PD as compared to normal subject...

Full description

Saved in:

Bibliographic Details
Main Author:	Manap, Hany Hazfiza
Format:	Thesis
Language:	English
Published:	2013
Online Access:	https://ir.uitm.edu.my/id/eprint/18311/2/TM_HANY%20HAFIZA%20MANAP%20EE%2013_5.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.18311
record_format	uketd_dc
spelling	my-uitm-ir.183112022-04-11T05:04:29Z Parkinson disease gait classification based on machine learning approach / Hany Hazfiza Manap 2013-12 Manap, Hany Hazfiza The aim of this thesis is to develop a Parkinson gait recognition technique that is able to evaluate and distinguish gait deviations experienced by Parkinson Disease (PD) patients from normal pattern. The research can be divided into two phase namely gait analysis of PD as compared to normal subjects, followed by gait classification using machine learning approach. Firstly, two types of statistical test are conducted which are independent t-test and Pearson’s correlation test. Raw gait database which consist of four basic gait features, five kinetic gait features and also twelve kinematic gait features are acquired from prior walking experiments of both PD and normal subjects. Based on statistical analysis conducted, significant different between PD and normal gait pattern are observed for four features, which are the step length and walking speed from basic features, maximum extension of hip from kinematic feature and maximum horizontal push-off force from kinetic feature. Hence these significant features are appropriate to be utilized for recognition of PD gait. Next, Principal Component Analysis (PCA) is used as feature extraction for each gait features from basic, kinetic and kinematic parameter followed by normalization based on intragroup as well as inter-group. To evaluate the effectiveness of each gait features category, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Naive Bayes classifier (NBC) are chosen as classifier. Results obtained demonstrated that for ANN classifier, fusion of basic and kinematic gait features due to intra-group normalization attained performance with 100% of accuracy outperformed others. As for SVM with polynomial kernel function, the finest performance specifically 100% accuracy is attained based on basic gait features from intra-group normalization whilst NBC achieved the best accuracy of 93.75% due to fusion of kinetic and kinematic gait features with intra-group normalization. Overall, the results obtained proven the ability of the machine classifiers in classifying gait pattern of PD from normal gait pattern with basic spatiotemporal seems to be the most reliable feature for this purpose due to its superb performance that achieved during classification by the three classifiers. 2013-12 Thesis https://ir.uitm.edu.my/id/eprint/18311/ https://ir.uitm.edu.my/id/eprint/18311/2/TM_HANY%20HAFIZA%20MANAP%20EE%2013_5.pdf text en public mphil masters Universiti Teknologi MARA Faculty of Electrical Engineering
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
description	The aim of this thesis is to develop a Parkinson gait recognition technique that is able to evaluate and distinguish gait deviations experienced by Parkinson Disease (PD) patients from normal pattern. The research can be divided into two phase namely gait analysis of PD as compared to normal subjects, followed by gait classification using machine learning approach. Firstly, two types of statistical test are conducted which are independent t-test and Pearson’s correlation test. Raw gait database which consist of four basic gait features, five kinetic gait features and also twelve kinematic gait features are acquired from prior walking experiments of both PD and normal subjects. Based on statistical analysis conducted, significant different between PD and normal gait pattern are observed for four features, which are the step length and walking speed from basic features, maximum extension of hip from kinematic feature and maximum horizontal push-off force from kinetic feature. Hence these significant features are appropriate to be utilized for recognition of PD gait. Next, Principal Component Analysis (PCA) is used as feature extraction for each gait features from basic, kinetic and kinematic parameter followed by normalization based on intragroup as well as inter-group. To evaluate the effectiveness of each gait features category, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Naive Bayes classifier (NBC) are chosen as classifier. Results obtained demonstrated that for ANN classifier, fusion of basic and kinematic gait features due to intra-group normalization attained performance with 100% of accuracy outperformed others. As for SVM with polynomial kernel function, the finest performance specifically 100% accuracy is attained based on basic gait features from intra-group normalization whilst NBC achieved the best accuracy of 93.75% due to fusion of kinetic and kinematic gait features with intra-group normalization. Overall, the results obtained proven the ability of the machine classifiers in classifying gait pattern of PD from normal gait pattern with basic spatiotemporal seems to be the most reliable feature for this purpose due to its superb performance that achieved during classification by the three classifiers.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Manap, Hany Hazfiza
spellingShingle	Manap, Hany Hazfiza Parkinson disease gait classification based on machine learning approach / Hany Hazfiza Manap
author_facet	Manap, Hany Hazfiza
author_sort	Manap, Hany Hazfiza
title	Parkinson disease gait classification based on machine learning approach / Hany Hazfiza Manap
title_short	Parkinson disease gait classification based on machine learning approach / Hany Hazfiza Manap
title_full	Parkinson disease gait classification based on machine learning approach / Hany Hazfiza Manap
title_fullStr	Parkinson disease gait classification based on machine learning approach / Hany Hazfiza Manap
title_full_unstemmed	Parkinson disease gait classification based on machine learning approach / Hany Hazfiza Manap
title_sort	parkinson disease gait classification based on machine learning approach / hany hazfiza manap
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Electrical Engineering
publishDate	2013
url	https://ir.uitm.edu.my/id/eprint/18311/2/TM_HANY%20HAFIZA%20MANAP%20EE%2013_5.pdf
_version_	1783733658310410240

Parkinson disease gait classification based on machine learning approach / Hany Hazfiza Manap

Similar Items