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

Ismail, Ahmad Puad (2018) Gait analysis and classification using front view markerless model / Ahmad Puad Ismail. In: The Doctoral Research Abstracts. IPSis Biannual Publication, 14 (14). Institute of Graduate Studies, UiTM, Shah Alam.

[img]
Preview
Text (ABSTRACT ONLY)
ABS_AHMAD PUAD ISMAIL TDRA VOL 14 IGS 18.pdf

Download (6MB) | Preview

Abstract

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.

Item Type: Book Section
Creators:
CreatorsEmail
Ismail, Ahmad PuadUNSPECIFIED
Subjects: H Social Sciences > HD Industries. Land use. Labor
Divisions: Institut Pengajian Siswazah (IPSis) : Institute of Graduate Studies (IGS)
Series Name: IPSis Biannual Publication
Volume: 14
Number: 14
Item ID: 22195
Uncontrolled Keywords: Abstract; Abstract of thesis; Newsletter; Research information; Doctoral graduates; IPSis; IGS; UiTM;
Last Modified: 11 Jan 2019 07:36
Depositing User: Staf Pendigitalan 2
URI: http://ir.uitm.edu.my/id/eprint/22195

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year