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Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions

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Robot Intelligence

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

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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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References

  1. Khoury, M., Liu, H.: Mapping fuzzy membership functions to normal distributions to understand boxing motions. In: Proceedings of UKCI 2008, De Montfort University, Leicester, UK (2008)

    Google Scholar 

  2. Khoury, M., Liu, H.: Fuzzy qualitative Gaussian inference: finding hidden probability distributions using fuzzy membership functions. In: IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS 2009) (2009)

    Google Scholar 

  3. Aggarwal, J.K., Cai, Q., Liao, W., Sabata, B.: Articulated and elastic non-rigid motion: a review. In: Proceedings of the IEEE Workshop on Motion of Non-Rigid and Articulated Objects, pp. 2–14 (1994)

    Google Scholar 

  4. Wang, L., Hu, W., Tan, T.: Recent developments in human motion analysis. Pattern Recognit. 36(3), 585–601 (2003)

    Article  Google Scholar 

  5. Chan, C.S., Liu, H., Brown, D.J.: Recognition of human motion from qualitative normalised templates. J. Intell. Robot. Syst. 48(1), 79–95 (2007)

    Article  Google Scholar 

  6. Yamato, J., Ohya, J., Ishii, K.: Recognizing human action in time-sequential images using hidden Markov model. In: IEEE Computer Vision and Pattern Recognition, pp. 379–385 (1992)

    Google Scholar 

  7. Remagnino, P., Tan, T.N., Baker, K.D.: Agent orientated annotation in model based visual surveillance. In: International Conference on Computer Vision, pp. 857–862 (1998)

    Google Scholar 

  8. Bobick, A.F., Wilson, A.D.: A state based technique for the summarization and recognition of gesture. In: International Conference on Computer Vision, pp. 382–388 (1995)

    Google Scholar 

  9. Pentland, A.P., Oliver, N., Brand, M.: Coupled hidden Markov models for complex action recognition. In: Massachusetts Institute of Technology, Media Lab (1996)

    Google Scholar 

  10. Guo, Y., Xu, G., Tsuji, S.: Understanding human motion patterns. In: International Conference on Pattern Recognition, pp. 325–329 (1994)

    Google Scholar 

  11. Yacoob, Y., Black, M.: Parameterized modeling and recognition of activities. In: Sixth International Conference on Computer Vision, pp. 120–127, 1998 (1998)

    Google Scholar 

  12. Galata, A., Johnson, N., Hogg, D.C.: Learning variable-length Markov models of behavior. Comput. Vis. Image Underst. 81(3), 398–413 (2001)

    Article  MATH  Google Scholar 

  13. Zhang, X., Naghdy, F.: Human motion recognition through fuzzy hidden Markov model. Proceedings of the International Conference on Computational Intelligence for Modelling 02, 450–456 (2005)

    Google Scholar 

  14. Lin, C.-T., Nein, H.-W., Lin, W.-C.: A space-time delay neural network for motion recognition and its application to lipreading. Int. J. Neural Syst. 9(4), 311–334 (1999)

    Article  Google Scholar 

  15. Anderson, D., Luke, R., Keller, J., Skubic, M.: Modeling human activity from voxel person using fuzzy logic. IEEE Trans. Fuzzy Syst. 17(1), 39–49 (2009)

    Article  Google Scholar 

  16. Calvert, T.W., Chapman, A.E.: Analysis and Synthesis of Human Movement, pp. 431–474 (1994)

    Google Scholar 

  17. Wu, G., et al.: ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion, part I: ankle, hip, and spine. J. Biomech. 35(4), 543–548 (2002)

    Article  Google Scholar 

  18. Wu, G., et al.: ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion, part II: shoulder, elbow, wrist and hand. J. Biomech. 38(5), 981–992 (2005)

    Article  Google Scholar 

  19. Crawford, N.R., Yamagushi, G.T., Dickman, C.A.: A new technique for determining 3-d joint angles: the tilt/twist method. Clin. Biomech. 17(2), 166 (1998)

    Article  Google Scholar 

  20. Dragulescu, D., Tascau, M., Stanciu, D.: Kinematic and dynamic modeling of human lower limb. In: IASTED International Conference Robotics and Applications, pp. 19–22 (2001)

    Google Scholar 

  21. Thingvold, J.: Biovision BVH format (1999). http://www.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff

  22. Favre, J., Aissaoui, R., Jolles, B.M., Siegrist, O., de Guise, J.A., Aminian, K.: 3d joint rotation measurement using mems inertial sensors: application to the knee joint. In: ISB-3D: 3-D Analysis of Human Movement, Valenciennes, France, 28–30 June 2006

    Google Scholar 

  23. ZADEH, L.: Fuzzy sets. Inf. Control 8, 338–353 (1986)

    Article  MathSciNet  Google Scholar 

  24. Sanghi, S.: Determining membership function values to optimize retrieval in a fuzzy relational database. In: Proceedings of the 2006 ACM SE Conference, vol. 1, pp. 537–542 (2006)

    Google Scholar 

  25. Iokibe, T.: A method for automatic rule and membership function generation by discretionary fuzzy performance function and its application to a practical system. In: Proceedings of the First International Joint Conference of the North American Fuzzy Information Processing Society Biannual Conference, pp. 363–364 (1994)

    Google Scholar 

  26. Kim, C., Russell, B.: Automatic generation of membership function and fuzzy rule using inductive reasoning. Third International Conference on Industrial Fuzzy Control and Intelligent Systems, IFIS’93, pp. 93–96 (1993)

    Google Scholar 

  27. Kim, J., Seo, J., Kim, G.: Estimating membership functions in a fuzzy network model for part-of-speech tagging. J. Intell. Fuzzy Syst. 4, 309–320 (1996)

    Google Scholar 

  28. Simon, D.: H infinity estimation for fuzzy membership function optimization. Int. J. Approx. Reason. 40(3), 224–242 (2005)

    Article  MATH  Google Scholar 

  29. Devi, B.B., Sarma, V.V.S.: Estimation of fuzzy memberships from histograms. Inf. Sci. 35(1), 43–59 (1985)

    Article  MATH  Google Scholar 

  30. Nieradka, G., Butkiewicz, B.S.: A method for automatic membership function estimation based on fuzzy measures. In: IFSA. Lecture Notes in Computer Science, vol. 4529, pp. 451–460. Springer, Berlin (2007)

    Google Scholar 

  31. Frantti, T.: Timing of fuzzy membership functions from data. Academic Dissertation, University of Oulu, Finland, July 2001

    Google Scholar 

  32. Khoury, M.: Pystep or python strongly typed genetic programming. Available online at: http://pystep.sourceforge.net/

  33. Lawrence, N.D.: Mocap toolbox for Matlab. Available on-line at http://www.cs.man.ac.uk/~neill/mocap/

  34. http://en.wikipedia.org/wiki/Boxing

  35. Darby, J., Li, B., Costen, N.: Activity classification for interactive game interfaces. Int. J. Comput. Games Technol. 2008(3), 1–7 (2008). http://dx.doi.org/10.1155/2008/751268

    Article  Google Scholar 

  36. Darby, J., Li, B., Costen, N.: Human activity recognition: Enhancement for gesture based game interfaces. In: 3rd International Conference on Games Research and Development, CyberGames (2007)

    Google Scholar 

  37. Liu, H.: A fuzzy qualitative framework for connecting robot qualitative and quantitative representations. IEEE Trans. Fuzzy Syst. 16(8), 1522–1530 (2008)

    Google Scholar 

  38. Liu, H., Brown, D.J., Coghill, G.M.: Fuzzy qualitative robot kinematics. IEEE Trans. Fuzzy Syst. 16(3), 808–822 (2008)

    Article  Google Scholar 

  39. Chang, C.S., Liu, H.: Fuzzy qualitative human motion analysis. IEEE Trans. Fuzzy Syst. 17(4), 851–862 (2009)

    Article  Google Scholar 

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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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Correspondence to Mehdi Khoury .

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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

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-328-2

  • Online ISBN: 978-1-84996-329-9

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