Predicting Post-Operative Patient Gait

Jongmin KimMovement Research Lab.Seoul National University

Problem statement• Predicting post-operative gait

• Possible approaches - Experience - Learning and prediction

Motion Data• Number of training data – DHL+RFT+TAL : 35 data– FDO+DHL+TAL+RFT : 33 data

• Total 13 joints

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.

}y,x{xy

Regressionprocess Predictor

New input data, x

Motiondatabase

Outputpose

Naïve linear regression• Direct regression analysis between input

and output.

• Minimize fitting error to obtain the predic-tor, .

A

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.

Advantages of using feature selec-tion

• Alleviating the effect of the curse of di-mensionality

• Improve a learning algorithm’s prediction performance

• Faster and more cost-effective• Providing a better understanding of the

data

L1 regularization• Effective feature selection method

• L1 norm: - It is the sum of the absolute value of each compo-

nent.

|||||| 1 i ixx

L1 regularization • Regularization based on the L1 drives maximizes sparseness.

• A new predicting post-operative gait can be estimated as matrix-vector multiplica-tion. - e.g.

L1 sparsity term

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.

Results

Future Work• Employing more training data

• Utilizing advanced statistical ap-proaches

• More comprehensive feature expla-nation