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A computer algorithm for representing spatial-temporal structure of human motion and a motion generalization method.

Woojin Park1, Don B Chaffin, Bernard J Martin

  • 1Department of Mechanical, Industrial, and Nuclear Engineering, University of Cincinnati, University and Campus Drive-626 Rhodes Hall, Cincinnati, OH 45221-0072, USA. woojin.park@uc.edu

Journal of Biomechanics
|September 13, 2005
PubMed
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This study introduces a symbolic motion structure representation (SMSR) algorithm to analyze human movement. The SMSR algorithm generalizes motion, enabling the creation of infinite motion variants for simulation and research.

Area of Science:

  • Robotics
  • Biomechanics
  • Human-Computer Interaction

Background:

  • Generalized Motor Program (GMP) theory provides a framework for understanding motor control.
  • Representing complex human motion for computational analysis remains a challenge.

Purpose of the Study:

  • To develop a novel algorithm for representing the spatial-temporal structure of human motion.
  • To enable the generalization of human motion for simulation and modification.

Main Methods:

  • The Symbolic Motion Structure Representation (SMSR) algorithm decomposes joint angle-time trajectories into symbolic motion segments.
  • Motion generalization involves relocating segment boundary points and rescaling segments.
  • A motion modification (MoM) algorithm is presented as an application.

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Main Results:

  • The SMSR algorithm successfully represents multi-joint human motion as a set of symbolic strings.
  • The motion generalization method allows for the creation of infinite motion variants.
  • The MoM algorithm demonstrates adaptation of reach motions for new targets.

Conclusions:

  • The SMSR algorithm offers a new method for symbolic representation of human motion structure.
  • Motion generalization based on SMSR provides a foundation for GMP-based simulation models.
  • This approach facilitates exploration of GMP theory through computational simulation and motion adaptation.