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.

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

• Tea-Ho Kang(Department of Industrial Engineering, Inha University) | 강태호 (인하대학교 산업경영공학과)
• Si-Yul Sung(Department of Industrial Engineering, Inha University) | 성시열 (인하대학교 산업경영공학과)
• Sang-Hyeok Han(Department of Industrial Engineering, Inha University) | 한상혁 (인하대학교 산업경영공학과)
• Dong-Hyun Park(Department of Industrial Engineering, Inha University) | 박동현 (인하대학교 산업경영공학과)
• Sungwoo Kang(Department of Industrial Engineering, Inha University) | 강성우 (인하대학교 산업경영공학과) Corresponding author