A Method for Lifelong Gesture Learning Based on Growing Neural Gas

  • Paul M. Yanik
  • Anthony L. Threatt
  • Jessica Merino
  • Joe Manganelli
  • Johnell O. Brooks
  • Keith E. Green
  • Ian D. Walker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8511)


Gesture-based interfaces offer the possibility of an intuitive command language for assistive robotics and ubiquitous computing. As an individual’s health changes with age, their ability to consistently perform standard gestures may decrease, particularly towards the end of life. Thus, such interfaces will need to be capable of learning commands which are not choreographed ahead of time by the system designers. This circumstance illustrates the need for a system which engages in lifelong learning and is capable of discerning new gestures and the user’s desired response to them. This paper describes an innovative approach to lifelong learning based on clustered gesture representations identified through the Growing Neural Gas algorithm. The simulated approach utilizes a user-generated reward signal to progressively refine the response of an assistive robot toward a preferred goal configuration.


machine learning gesture recognition human-robot interaction assistive robotics 


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  1. 1.
    Hamker, F.H.: Life-long learning Cell Structures-continuously learning without catastrophic interference. Neural Networks 14(4), 551–573 (2001)CrossRefGoogle Scholar
  2. 2.
    Yanik, P.M., Merino, J., Threatt, A.L., Manganelli, J., Brooks, J.O., Green, K.E., Walker, I.D.: A Gesture Learning Interface for Simulated Robot Path Shaping with a Human Teacher. IEEE Transactions on Human-Machine Systems 44(1), 41–54 (2014)CrossRefGoogle Scholar
  3. 3.
    Fritzke, B.: A Growing Neural Gas Network Learns Topologies. Advances in Neural Information Processing Systems 7(7), 625–632 (1995)Google Scholar
  4. 4.
    Grossberg, S.: Nonlinear neural networks: Principles, mechanisms, and architectures. Neural Networks 1(1), 17–61 (1988)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Kohonen, T.: The self-organizing map. Proc. of the IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  6. 6.
    Holmström, J.: Growing Neural Gas: Experiments with GNG, GNG with Utility and Supervised GNG. Master’s thesis, Uppsala University – Department of Information Technology (2002)Google Scholar
  7. 7.
    Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 613–618. Springer, Heidelberg (1997)Google Scholar
  8. 8.
    Furao, S., Hasegawa, O.: An incremental network for on-line unsupervised classification and topology learning. Neural Networks 19(1), 90–106 (2006)CrossRefzbMATHGoogle Scholar
  9. 9.
  10. 10.
    Rao, C., Yilmaz, A., Shah, M.: View-Invariant Representation and Recognition of Actions. International Journal of Computer Vision 50(2), 203–226 (2002)CrossRefzbMATHGoogle Scholar
  11. 11.
    Microsoft Xbox 360 + Kinect Website,
  12. 12.
    Kaplan, F., Oudeyer, P.Y., Kubinyi, E., Miklósi, A.: Robotic clicker training. Robotics and Autonomous Systems 38(3), 197–206 (2002)CrossRefGoogle Scholar
  13. 13.
    Yanik, P.M.: Gesture-Based Robot Path Shaping. PhD thesis, Clemson University (2013)Google Scholar
  14. 14.
    Touzet, C.F.: Neural reinforcement learning for behaviour synthesis. Robotics and Autonomous Systems 22(3), 251–281 (1997)CrossRefGoogle Scholar
  15. 15.
    Estrada, E.: The Structure of Complex Networks: Theory and Applications. Oxford (2012)Google Scholar
  16. 16.
    Tucker, A.: Applied Combinatorics, 6th edn. Wiley (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Paul M. Yanik
    • 1
  • Anthony L. Threatt
    • 2
  • Jessica Merino
    • 2
  • Joe Manganelli
    • 3
  • Johnell O. Brooks
    • 4
  • Keith E. Green
    • 3
  • Ian D. Walker
    • 2
  1. 1.Department of Engineering and TechnologyWestern Carolina UniversityCullowheeUSA
  2. 2.Department of Electrical and Computer EngineeringClemson UniversityClemsonUSA
  3. 3.School of ArchitectureClemson UniversityClemsonUSA
  4. 4.Department of Automotive EngineeringClemson UniversityClemsonUSA

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