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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)

Abstract

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.

Keywords

machine learning gesture recognition human-robot interaction assistive robotics 

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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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