Machine learning based gait parameter analysis for normal and pathological patterns
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Zhang, Wenwen
issued: 2023-05-03T21:43:05Z
issued: 2023
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: The pervasiveness of wearable sensors has contributed to plenty of daily activity data and greatly improved the ability to link data analytics with healthcare applications. Gait analysis is an important part of the healthcare field to help monitor the patient’s progress with gait disturbances such as Stroke and Parkinson’s. The traditional method to get gait parameters is the GaitRite walkway, which is cumbersome and requires professional training in setting up every time. With the assistance of wearable sensors, we are able to record the motion of the foot without those limitations. Zero Update Position and Timing (ZUPT) has been used to analysis of normal people’s walking for a long time. However, ZUPT has been validated on the walking of normal people only. In this article, we test the ZUPT on pathological patients. We compare the results of normal and abnormal walking patterns in terms of step recognition and gait parameter prediction accuracy. To help track and monitor patient disease progress with better accuracy, we propose Machine Learning (ML) algorithms to improve the accuracy of healthcare application estimation results based on the time series data. Those patients also suffer from gait impairments which may cause fatal dangers such as falls. In the meantime, gait parameters are also significant signs of the progress of the disease. To monitor the gait condition, we collect datasets with different gaits, comparing and predicting gait parameters for the patients. Compared to the ZUPT algorithm, the final prediction from ML algorithm of gait parameters achieves greater performance.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/84558
note: Machine Learning based Gait Parameter Analysis forNormal and Pathological PatternsbyWenwen ZhangBsc. Electric Engineering, Tianjin University, 2020A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of Applied ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Electric and Computer Engineering)The University of British Columbia(Vancouver)April 2023© Wenwen Zhang, 2023The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Machine Learning based Gait Parameter Analysis for Normal and Patho-logical Patternssubmitted by Wenwen Zhang in partial fulfillment of the requirements for the de-gree of Master of Applied Science in Electric and Computer Engineering.Examining Committee:Prof. Peyman Servati, Professor, Electric and Computer Engineering, UBCSupervisorProf. Calvin Kuo, Assistant Professor, Biomedical Engineering, UBCSupervisory Committee MemberProf. Jane Z. Wang, Professor, Electric and Computer Engineering, UBCSupervisory Committee MemberiiAbstractThe pervasiveness of wearable sensors has contributed to plenty of daily activitydata and greatly improved the ability to link data analytics with healthcare applica-tions. Gait analysis is an important part of the healthcare field to help monitor thepatient’s progress with gait disturbances such as Stroke and Parkinson’s. The tradi-tional method to get gait parameters is the GaitRite walkway, which is cumbersomeand requires professional training in setting up every time. With the assistance ofwearable sensors, we are able to record the motion of the foot without those limi-tations.Zero Update Position and Timing (ZUPT) has been used to analysis of normalpeople’s walking for a long time. However, ZUPT has been validated on the walkingof normal people only. In this article, we test the ZUPT on pathological patients.We compare the results of normal and abnormal walking patterns in term…
has type: Thesis/Dissertation
date issued: 2023-11
is shown at: 10.14288/1.0431601
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Servati, Peyman
type: Text
identifier URI: http://hdl.handle.net/2429/84558