Predicting Post-Operative P atient Gait

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 Output pose Motion database

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