Developing Degenerative Arthritis Patient Classification Algorithm based on 3D Walking Video

서지정보

ㆍ발행기관 : 한국산업경영시스템학회 ㆍ수록지정보 : 산업경영시스템학회지 / 46권 / 3호
ㆍ저자명 : Tea-Ho Kang, Si-Yul Sung, Sang-Hyeok Han, Dong-Hyun Park, Sungwoo Kang

목차

1. 서 론
2. 이론적 배경
2.1 퇴행성 관절염 환자 분류 시스템
2.2 3D Depth Camera
2.3 분산분석, 일원배치법
3. 연구 방법
3.1 데이터 수집
3.2 특성 선정
3.3 모델 학습
3.4 특성별 분류 알고리즘
4. 연구 결과
5. 결 론
Acknowledgement

영어 초록

Degenerative arthritis is a common joint disease that affects many elderly people and is typically diagnosed through radiography. However, the need for remote diagnosis is increasing because knee pain and walking disorders caused by degenerative arthritis make face-to-face treatment difficult. This study collects three-dimensional joint coordinates in real time using Azure Kinect DK and calculates 6 gait features through visualization and one-way ANOVA verification. The random forest classifier, trained with these characteristics, classified degenerative arthritis with an accuracy of 97.52%, and the model's basis for classification was identified through classification algorithm by features. Overall, this study not only compensated for the shortcomings of existing diagnostic methods, but also constructed a high-accuracy prediction model using statistically verified gait features and provided detailed prediction results.

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