Predicting Post-Operative Gait of Cerebral Palsy Patients

1. Predicting Post-Operative Gait of Cerebral Palsy Patients Movement Research Lab. Seoul National University

2. Motivation • We want to predict the gait of post-operative patients to compensate doctors’ experience.

3. Our goal • Predicting post-operative gait • learning from pre & post operative gait data

4. Motion predictor • Learn a motion predictor from training data set . - : pre-operative gait (input) - : post-operative gait (output) • Given new input data, we generate a new motion using the learned predictor. Regression process Predictor New pre-operative gait, x New post- operative gait Training data

5. Regression process Pre-operative gait . . . i • Regression • process Motion to motion i . . . Post-operative gait

6. Canonical Correlation Analysis (CCA) • Find pairs of basis that maximize the correlation between two variablesin the reduced space. Regression Variable X Variable Y Reduced Y Projection Basis X, CCA Basis Y Reduced X Correlation :

7. Sparse CCA Reformulation

8. CCA-based regression • Linear regression between pair of reduced data • Reconstruction from subspace to original space • Concatenating these matrix produces the predictor Linear Regression Reduced motion data Reduced input data Linear Regression Reduced motion data Original motion data

9. Motion synthesis Orientations of all joints Projection to the acquired basis Prediction matrix Pre-operative gait Post-operative gait

10. Result - method comparison

11. GCD Data normalization

12. Design X & Y Pre-operative Post-operative C3D C3D GCD GCD

13. Result - feature graph

14. Thank You • Q & A

15. Gait of cerebral palsy patient 출처: http://www.youtube.com/watch?v=q7AokhnifG0

16. Treatments

17. What is Cerebral palsy ? • Cerebral palsy (뇌성마비) - 태아의 뇌에서 발생하는 비진행성 장애에 의한 활동 제한으로동작과 자세에 영향을 미치는 영구적인 이상

18. Orthopedic surgery • Distal Hamstring Lengthening (DHL) • Rectus FemorisTransfer (RFT) • TendoAchilles Lengthening (TAL) • Femoral DerotationOsteotomy (FDO) [Poses of cerebral palsy patients]

19. Related work • “Predicting outcomes of rectus femoris transfer surgery” [Reinbolt et al. 2009] • Select a set of preoperative gait features that distinguished between good and poor outcomes • “Evaluation of conventional selection criteria for lengthening for individuals with cerebral palsy” [Truong etal. 2011]

20. Related work [Chai and Hodgins 2005] [Slyper and Hodgins 2008] [Seol et al. 2013] [Kim et al. 2012]

21. Motion data • Number of patients • DHL+RFT+TAL : 35 • FDO+DHL+TAL+RFT : 33 • Total seven joints right femur left femur pelvis right knee left knee right foot left foot

22. Naïve linear regression • Direct regression analysis between pre and post-operative gait • Minimize fitting error to obtain the predictor, . Problems ? large prediction error

23. Result :motion to motion + naïve

24. Minimizing prediction error • Dimension reduction • Fully explains the nonlinear relationship between training input and output - Nonlinear dimension reduction method

25. PCA • PCA : maximum variance projection method. Data dependency ? Reduced Y Projection Variable X Variable Y Reduced X

26. Kernel CCA • CCA may not fully explain non-linear relationship between pre-operative and post-operative motion. • Non-linear CCA using the kernel trick method • Transform the data into high dimensional space Substitute non-linear mapping into CCA

27. Future work • Design training input & output with respect to the clinical context. • Feature selection - Alleviating the effect of the curse of dimensionality - Improve a prediction performance - Faster and more cost-effective - Providing a better understanding of the data