Classifying Kung-Fu Side kicksWith low cost hardware and open source software

Victoria VærnøSchool of Computer Science & Engineering, Seoul National University

victoriavarno@gmail.com

Method

Iterations & Results

References

Data

Biointelligence Lab, Seoul National University, Seoul 151-744, Korea (http://bi.snu.ac.kr)

Background

Research Questions

• Motion capture technology and machine learning workbenches are accessible to the greater public, not just computer scientists anymore.

• Amateurs and professionals are responding to the need for open source technology, but links between cheap hardware (like web-cams and Kinect) and open source software is still hard to find for motion capture.

• This project documents a basic experiment connecting Kinect with open source software and machine learning theory.

For a version of the multilayer perceptron algorithm over my motion capture data:• For separately created unseen data, what accuracy and “bad”-class label precision

can be obtained?• Are these results judged good enough to use in a beta production of an automatic

feedback application for amateur Kung-Fu training purposes?• What challenges follow a relatively limited training set and what basic machine

learning techniques can reduce these?

Iterations of experiment and analysis to acquire deeper understanding of the data and find the best machine learning technique to make a predictor based on the data properties:• Capture the kick-data and identify the class for each kick.• Run iterations of

Model the data with parameter-tuned machine learning technique in WekaTest on unseen data and analyzeChange data and/or machine learning technique

Main focus on the Multilayered Perceptron algorithm with variations.

Meta-Learner

Clustering

Result- and Data Analysis Tests

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Set 1: Training data with kicks by Victoria. Set 2: Test set with kicks by Victoria in different circumstances than the training data. Set 3: Kicks by the man Svenn.

Set

1 Set

2 Set

3Accuracy

97%

Precisionof ”Bad”

1

MLP - 10-fold cross validation

Yes,Unbal-anced.

Unbalanced Data?

• Blog with guides to getting started on the more advanced features of Weka:http://ianma.wordpress.com/category/weka/

• Witten I. H. & Eibe F. & Hall M. A. (2011). Data mining : practical machine learning tools and techniques. — 3rd ed. Chapter 8,11. Morgan Kaufmann Publishers

• Mitchell T.M. (1997). Machine Learning. Chapter 1,3,4,5,8. McGraw-Hill Science/Engineering/Math

• Nilsson N. J. (2009). The Quest for Artificial Intelligence. Cambridge University Press. Can befound free at http://ai.stanford.edu/~nilsson/QAI/qai.pdf

• Weka homepage: http://www.cs.waikato.ac.nz/ml/weka/• Help with code from the open source community: http://stackoverflow.com/

Accuracy84%

Precisionof ”Bad”

1

Tes t

Ad-aBoostM1

Average,meanaccuracy

89%Precisionof ”Bad”

1

After only half of Data Set 3 was added to the training set, prediction accuracy of the model on Data Set 2 increased by 5-9%. The boosting algorithm typically increasedthe results of it’s base classifier by 2-3%.Lessons learned• It is very hard for one person to create unbalanced motion data.• Using a boosting stratagy to combat unbalanced motion capture data does have

some positive effect, 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

These results are promising for further investigation in machine learning for motion capturing with low cost hardware and open source software. However, the unseen test case is by a person also represented in the training data. Classifying unseen people’s kicks remain unexplored, but light experimentation suggests that adding just a few kicks by new people to the training data greatly increases the model’s generalizability.

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

Clustering K-means K=3