predicting post-operative patient gait jongmin kim movement research lab. seoul national university
DESCRIPTION
Motion Data Number of training data –DHL+RFT+TAL : 35 data –FDO+DHL+TAL+RFT : 33 data Total 13 jointsTRANSCRIPT
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Predicting Post-Operative Patient Gait
Jongmin KimMovement Research Lab.Seoul National University
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Problem statement• Predicting post-operative gait
• Possible approaches - Experience - Learning and prediction
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Motion Data• Number of training data – DHL+RFT+TAL : 35 data– FDO+DHL+TAL+RFT : 33 data
• Total 13 joints
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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
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Naïve linear regression• Direct regression analysis between input
and output.
• Minimize fitting error to obtain the predic-tor, .
A
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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.
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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
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L1 regularization• Effective feature selection method
• L1 norm: - It is the sum of the absolute value of each compo-
nent.
|||||| 1 i ixx
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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
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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.
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Results
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Future Work• Employing more training data
• Utilizing advanced statistical ap-proaches
• More comprehensive feature expla-nation