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Extracting Motion Primitives from Natural Handwriting Data

  • Ben H. Williams
  • Marc Toussaint
  • Amos J. Storkey
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

For the past 10 years it has become clear that biological movement is made up of sub-routine type blocks, or motor primitives, with a central controller timing the activation of these blocks, creating synergies of muscle activation. This paper shows that it is possible to use a factorial hidden Markov model to infer primitives in handwriting data. These primitives are not predefined in terms of location of occurrence within the handwriting, and they are not limited or defined by a particular character set. Also, the variation in the data can to a large extent be explained by timing variation in the triggering of the primitives. Once an appropriate set of primitives has been inferred, the characters can be represented as a set of timings of primitive activations, along with variances, giving a very compact representation of the character. Separating the motor system into a motor primitive part, and a timing control gives us a possible insight into how we might create scribbles on paper.

Keywords

Hide State Muscle Synergy Motor Timing Primitive Activation Motor Primitive 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ben H. Williams
    • 1
  • Marc Toussaint
    • 1
  • Amos J. Storkey
    • 1
  1. 1.Institute of Adaptive and Neural ComputationUniversity of Edinburgh, School of InformaticsEdinburghUK

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