1. 연구 계획 이경호 2012. 5. 1

2. Predicting outcome of surgery Ideal approach.. ? Training Data Predicting outcome surgery

3. Process overview Pre/Post operative data Features Training Features Input pre- operative data Predicting Features

4. Process overview Pre-operative data Unfiltered Features - Rotation, Flexion, Extension, Moment, Power - Single value, average, min/max, … - Swing phase, Double support - … Filter-based features - knee flexion angle's 5 th value - foot rotation moment’s 7 th value - average knee extension moment in early swing - peak knee flexion in swing phase - … Post-operative data Predicting Features - peak knee flexion in swing phase - knee range of motion in early swing - total knee range of motion - timing of peak knee flexion during swing phase - …

5. Training Process overview Pre-operative data Post-operative data Predicting Features - peak knee flexion in swing phase - knee range of motion in early swing - total knee range of motion - timing of peak knee flexion during swing phase - … Filter-based features - knee flexion angle's 5 th value - foot rotation moment’s 7 th value - average knee extension moment in early swing - peak knee flexion in swing phase - …

6. Testing Process overview Input pre-operative data Post-operative data Predicting Features - peak knee flexion in swing phase - knee range of motion in early swing - total knee range of motion - timing of peak knee flexion during swing phase - … Filter-based features - knee flexion angle's 5 th value - foot rotation moment’s 7 th value - average knee extension moment in early swing - peak knee flexion in swing phase - …

7. Current Dataset GCD File ( Gait Cycle Data ).. surgery..

8. Predicting Outcomes Predict what? –Good / Poor outcomes –Numerical value of features Which feature? –Motion Data

9. Predicting Outcomes 여러 수술의 혼재 – FDO+DHL+TAL+RFT +α – DHL+TAL+RFT +α – 추가적인 정보의 필요성

10. Performance evaluation Repeated hold-out method