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Gait analysis and classification using front view markerless model / Ahmad Puad Ismail

Gait abnormality recognition would be very useful in medical monitoring and surveillance systems. The analysis can be used as one of the surveillance methods, medical rehabilitation monitoring and early detection in possible gait related symptoms. Existing manual observation can only be done by prof...

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Main Author: Ismail, Ahmad Puad
Format: Thesis
Language:English
Published: 2018
Subjects:
HD Industries. Land use. Labor
Online Access:https://ir.uitm.edu.my/id/eprint/99945/1/99945.pdf
https://ir.uitm.edu.my/id/eprint/99945/
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spelling my.uitm.ir.999452024-08-07T15:05:44Z https://ir.uitm.edu.my/id/eprint/99945/ Gait analysis and classification using front view markerless model / Ahmad Puad Ismail Ismail, Ahmad Puad HD Industries. Land use. Labor Gait abnormality recognition would be very useful in medical monitoring and surveillance systems. The analysis can be used as one of the surveillance methods, medical rehabilitation monitoring and early detection in possible gait related symptoms. Existing manual observation can only be done by professionals and might cause misidentification on the real condition or situation of the subject. Additionally, gait laboratory utilises very costly motion systems for gait acquisition as research database. Hence, there is a need to produce a low cost abnormal gait detection method. In this research, analysis of front view human gait silhouette was done to investigate the possibility of a method to be developed in recognizing abnormality on proposed model-based approach. The model based which utilised the pendulum and hexagonal theorem as feature extraction method were used to produce the vertical angles of both hip and knee for 70 image sequences as feature vectors for both legs for one complete gait cycle sequence. Consequently, 280 features generated based on four parameters from the lower limb of human body for gait abnormality detection. On top of that, the gait features extracted from different gait patterns namely normal, drunken, dragging and tiptoed were classified as either normal or abnormal using four different classifiers namely ANN, KNN, SVM and Bayesian. Results attained showed that the proposed method was indeed suitable as gait abnormality recognition based on human gait pattern with the result of SVM as 90.9 percent leading the other classifier for pendulum features, whilst both ANN and SVM classification rate shows the highest for hexagonal features with also 90.9 percent after normalization and feature selection. Further, the proposed method namely the markerless front view modelling for abnormal gait detection was evaluated using hardware based. The hardware utilised a Linux based embedded board such as Raspberry Pi and Beaglebone, with Python software programming for recognising the differences between normal and abnormal gait based on gait image as input sequences captured from camera. Classification rate obtained were similar using these two boards namely 84.21% for SVM and 89.47% for KNN classifiers. In addition, processing time taken using Beaglebone Black board was higher that was approximately one minute as compared to Raspberry Pi that required longer time. 2018 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/99945/1/99945.pdf Gait analysis and classification using front view markerless model / Ahmad Puad Ismail. (2018) PhD thesis, thesis, Universiti Teknologi MARA (UiTM). <http://terminalib.uitm.edu.my/99945.pdf>
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
topic HD Industries. Land use. Labor
spellingShingle HD Industries. Land use. Labor
Ismail, Ahmad Puad
Gait analysis and classification using front view markerless model / Ahmad Puad Ismail
description Gait abnormality recognition would be very useful in medical monitoring and surveillance systems. The analysis can be used as one of the surveillance methods, medical rehabilitation monitoring and early detection in possible gait related symptoms. Existing manual observation can only be done by professionals and might cause misidentification on the real condition or situation of the subject. Additionally, gait laboratory utilises very costly motion systems for gait acquisition as research database. Hence, there is a need to produce a low cost abnormal gait detection method. In this research, analysis of front view human gait silhouette was done to investigate the possibility of a method to be developed in recognizing abnormality on proposed model-based approach. The model based which utilised the pendulum and hexagonal theorem as feature extraction method were used to produce the vertical angles of both hip and knee for 70 image sequences as feature vectors for both legs for one complete gait cycle sequence. Consequently, 280 features generated based on four parameters from the lower limb of human body for gait abnormality detection. On top of that, the gait features extracted from different gait patterns namely normal, drunken, dragging and tiptoed were classified as either normal or abnormal using four different classifiers namely ANN, KNN, SVM and Bayesian. Results attained showed that the proposed method was indeed suitable as gait abnormality recognition based on human gait pattern with the result of SVM as 90.9 percent leading the other classifier for pendulum features, whilst both ANN and SVM classification rate shows the highest for hexagonal features with also 90.9 percent after normalization and feature selection. Further, the proposed method namely the markerless front view modelling for abnormal gait detection was evaluated using hardware based. The hardware utilised a Linux based embedded board such as Raspberry Pi and Beaglebone, with Python software programming for recognising the differences between normal and abnormal gait based on gait image as input sequences captured from camera. Classification rate obtained were similar using these two boards namely 84.21% for SVM and 89.47% for KNN classifiers. In addition, processing time taken using Beaglebone Black board was higher that was approximately one minute as compared to Raspberry Pi that required longer time.
format Thesis
author Ismail, Ahmad Puad
author_facet Ismail, Ahmad Puad
author_sort Ismail, Ahmad Puad
title Gait analysis and classification using front view markerless model / Ahmad Puad Ismail
title_short Gait analysis and classification using front view markerless model / Ahmad Puad Ismail
title_full Gait analysis and classification using front view markerless model / Ahmad Puad Ismail
title_fullStr Gait analysis and classification using front view markerless model / Ahmad Puad Ismail
title_full_unstemmed Gait analysis and classification using front view markerless model / Ahmad Puad Ismail
title_sort gait analysis and classification using front view markerless model / ahmad puad ismail
publishDate 2018
url https://ir.uitm.edu.my/id/eprint/99945/1/99945.pdf
https://ir.uitm.edu.my/id/eprint/99945/
_version_ 1806783731891961856
score 13.211869

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