DGIST Scholar: Classification of Gait Type in Hip Osteoarthritis Patients using Principal Component Analysis and Gaussian Mixture Model

Title

Classification of Gait Type in Hip Osteoarthritis Patients using Principal Component Analysis and Gaussian Mixture Model

Alternative Title

주성분 분석(Principal Component Analysis)과 가우시안 혼합 모델(Gaussian Mixture Model)을 이용한 고관절 퇴행성 관절염 환자들의 보행 분류

DGIST Authors

Hayoon Lee ; Sehoon Oh ; Sang Hyun Park

Advisor

오세훈

Co-Advisor(s)

Sang Hyun Park

Issued Date

2021

Awarded Date

2021/02

Citation

Hayoon Lee. (2021). Classification of Gait Type in Hip Osteoarthritis Patients using Principal Component Analysis and Gaussian Mixture Model. doi: 10.22677/thesis.200000361643

Type

Thesis

Subject

Gait classification, Principal component analysis, Gaussian mixture model, OpenSim, 보행 분류, 주성분 분석, 가우시안 혼합 모델

Abstract

This paper proposes and compares two approaches that can identify different gait types of patients with hip osteoarthritis (OA) quantitatively using machine learning techniques. One is a simple and intuitive method that does not need clustering steps, and the other is a more detailed classification method that subdivides the gait classification results of the first method.
Force plate measurements of 22 patients with hip OA and 18 healthy subjects without surgical history were collected and analyzed using principal component analysis (PCA) and Gaussian Mixture Model (GMM) to identify different types of gait. The physical meanings of the identified gait types are explained using the latent features of gait obtained from PCA and muscle forces calculated using OpenSim.
The approaches will not only be useful for understanding the gait patterns of patients with hip OA but also will be applicable in analyzing different types of gait other than those of patients with hip OA.

Table Of Contents

I Introduction 1
II Method 2
2.1 Subjects 2
2.2 Instrument 2
2.3 Experiment protocol 4
2.4 Measured variables 4
2.5 Institutional review board 4
2.6 Dimension reduction methods 5
2.7 Similarity of gait patterns 7
2.8 Clustering methods 8
2.9 Muscle force estimation 9
III Result 10
3.1 Gait type classification using similarity of principal components 10
3.2 Gait type classification using Gaussian Mixture Model 12
3.3 Muscle forces of the identified gait types 15
IV Discussion 46
4.1 Comparison of the identified gait types 46
4.1.1 Characteristics of the similarity-based groups 46
4.1.2 Characteristics of the component-based groups 49
4.1.3 Comparison of the similarity-based and component-based groups 50
4.2 Physical implications of the gait types 51
4.2.1 Muscle forces of the gait types in spatial aspect 51
4.2.2 Muscle forces of the gait types in temporal aspect 54
4.3 Comparison of gait type classification methods 55
V Conclusion 57
References 58

URI

http://dgist.dcollection.net/common/orgView/200000361643
http://hdl.handle.net/20.500.11750/16644

DOI

10.22677/thesis.200000361643

Degree

Master

Department

Robotics Engineering

Publisher

DGIST

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