Abstract
This research introduces and builds on the concept of Fuzzy Gaussian Inference (FGI) (Khoury and Liu in Proceedings of UKCI, 2008 and IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS 2009), 2009) as a novel way to build Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions. This method is now combined with a Genetic Programming Fuzzy rule-based system in order to classify boxing moves from natural human Motion Capture data. In this experiment, FGI alone is able to recognise seven different boxing stances simultaneously with an accuracy superior to a GMM-based classifier. Results seem to indicate that adding an evolutionary Fuzzy Inference Engine on top of FGI improves the accuracy of the classifier in a consistent way.
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Acknowledgements
The project is funded by EPSRC Industrial CASE studentship and MM2G Ltd under grant No. 07002034. Many thanks to Portsmouth University Boxing Club and to the Motion capture Team: Alex Counsell, Geoffrey Samuel, Ollie Seymour, Ian Sedgebeer, David McNab, David Shipway and Maxim Mitrofanov.
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Khoury, M., Liu, H. (2010). Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions. In: Liu, H., Gu, D., Howlett, R., Liu, Y. (eds) Robot Intelligence. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84996-329-9_5
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DOI: https://doi.org/10.1007/978-1-84996-329-9_5
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