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

1. Classifying Kung-Fu Side kicks With low cost hardware and open source software Victoria Værnø School of Computer Science & Engineering, Seoul National University victoriavarno@gmail.com Background Iterations & Results • 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. MLP - 10-fold cross validation Accuracy 97% Precision • of”Bad” 1 Research Questions • 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? Tes t Clustering K-means K=3 Unbalanced Data? Method • 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 Weka • Test on unseen data and analyze • Change data and/or machine learning technique • Main focus on the Multilayered Perceptron algorithm with variations. Accuracy 84% Precision • of”Bad” 1 Yes, Unbal-anced. AdaBoostM1 Meta- Learner Clustering Average, mean accuracy 89% Precision • of”Bad” 1 • Afteronly half of Data Set 3 wasadded to the training set, predictionaccuracyofthemodelon Data Set 2 increased by 5-9%. The boostingalgorithmtypicallyincreased • theresultsofit’s base classifierby 2-3%. • Lessonslearned • It is very hard for one person to createunbalanced motion data. • Using a boostingstratagy to combatunbalanced motion capture data does have somepositive effect, butadding a different person’s motion is far more efficient. • ”Spend time gathering more data ratherthan tuning a particularmethod” Nilsson N.J • Theseresultsarepromising for furtherinvestigation in machinelearning for motion capturingwithlowcost hardware and opensourcesoftware. However, theunseentest case is by a person alsorepresented in the training data. Classifyingunseenpeople’skicks remainunexplored, butlightexperimentationsuggeststhatadding just a few kicks by newpeople to the training data greatlyincreasesthemodel’sgeneralizability. Result- and Data Analysis Tests Data Set 1: Training data with kicks by Victoria. Set 2: Test setwith kicks by Victoria in different circumstancesthanthe training data. Set 3: Kicks by the man Svenn. Set 2 Set 3 Set 1 References • Blogwithguides to gettingstartedonthe more advancedfeaturesofWeka: • http://ianma.wordpress.com/category/weka/ • Witten I. H. & Eibe F. & Hall M. A. (2011). Data mining : practicalmachinelearningtoolsand 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/ Data attributes: 18 joints * 3 dimensions* 6 frames per movie+ 1 classlable = 325 attributes .bvh .arff Biointelligence Lab, Seoul National University, Seoul 151-744, Korea (http://bi.snu.ac.kr)