predicting post-operative p atient gait
DESCRIPTION
Predicting Post-Operative P atient Gait . Jongmin Kim Movement 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 - PowerPoint PPT PresentationTRANSCRIPT
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.
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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