Machine learning with Weka for Kung fu - training purposes

Machine learning with Weka for Kung fu-training

purposes

Victoria Værnøvictoriavarno@gmail.comvaernoe.wordpress.com

BackgroundMotion capture and machine learning workbenches are accessible to the public.

Links between cheap hardware (eg. web-cams and Kinect) & open source machine learning software is still hard to find for motion capture.

Research focus

• Build a model which is good enough to consider for a beta application for amateur Kung-Fu training purposes.– For separately created unseen data, high accuracy

and high “bad”-class label precision• What challenges follow a relatively limited

training set and what basic machine learning techniques can reduce these?

Meta-Learner

Clustering

Result- and Data

Analysis

Test

Method

Data

Data attributes: 18 joints * 3 dimensions * 6 frames per movie + 1 class lable = 325 attributes

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.arff

Iteration 1

Accuracy97%

Precisionof ”Bad”

1

Good! Too good?

Generate MLP model on the training data.

Iteration 1

Accuracy85%

Precisionof ”Bad”

1

Not so great. Unbalanced dataset?

Test the model with unseen dataset.

Class 1 Attribute values which in reality define class 1

Attribute values which in reality define class 2

Attribute values which very often arise in class 1, but does not define class 1.

Unbalanced Data

Class 2

This part is much bigger in the test set and

real life.

Iteration 2

Clustering K-means K=3

Trying to compensate for unbalanced data

• Meta-classifier AdaBoostM1 boosting algorithm• Collecting more data – new people.• Other suggestions, please tell or email me!

(victoriavarno@gmail.com)

Iteration 3

AdaBoostM1

+

Just MLP+ Data set 1

AdaBoostM1+ Data set 1

Just MLP + Data set 1+ Data set 3

AdaBoostM1+ Data set 1+ Data set 3

84%

80%

89%

86%

Generate models and test on Data set 2.

• Promising results for further investigation.– Cross-validation over 90%– ”Bad” class label always precision = 1– Adding just one more person -> unseen data 89%

• Classifying unseen people’s kicks remain unexplored.

• Data collection: Hard for one person to create balanced motion data.

• Modeling: Boosting strategy to combat unbalanced motion capture data works in some cases, but adding a different person’s motion is far more efficient.

”Spend time gathering more data rather than tuning a particular method” Nilsson N.J

Conclusion

Lessons Learned

Thank You!

Q?