1. Predicting Post-Operative Patient Gait Jongmin Kim Movement Research Lab. Seoul National University

2. Problem statement Predicting post-operative gait Possible approaches - Experience - Learning and prediction

3. Motion Data Number of training data –DHL+RFT+TAL : 35 data –FDO+DHL+TAL+RFT : 33 data Total 13 joints

4. Pose predictor Learn a pose predictor from training data set. - : pre-operative patient’ pose (input) - : post-operative patient’ pose (output) Given new input data, we generate new character pose using the learned predictor. Regression process Predictor New input data, x Motion database Output pose

5. Naïve linear regression Direct regression analysis between input and output. Minimize fitting error to obtain the predictor,.

6. Data & Feature Many data has hundreds of variables with many irrelevant and redundant ones. Feature is variables obtained by erasing redundant / noise variables from data.

7. Advantages of using feature selection Alleviating the effect of the curse of dimensionality Improve a learning algorithm’s prediction performance Faster and more cost-effective Providing a better understanding of the data

8. L1 regularization Effective feature selection method L1 norm: - It is the sum of the absolute value of each component.

9. L1 regularization Regularization based on the L1 drives maximizes sparseness. A new predicting post-operative gait can be estimated as matrix-vector multiplication. - e.g. L1 sparsity term

10. L1 regularization With the learned model, we can fully explain the features for each body joints. - Features can be considered as the combination of the joint information corresponding non-zero terms in the row vector of the learned model. - e.g. left knee position = 0.4 * left ankle position + 0.6 * pelvis position.

11. Results

12. The most movable joints are selected as features for almost every joint. The most closest joints are selected as features for almost every joint.

13. Future Work Employing more training data Utilizing advanced statistical approaches More comprehensive feature explanation