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Related Experiment Videos

Basic functions of variability of simple pre-planned movements.

S R Gutman1, G L Gottlieb

  • 1Department of Physiology, Rush Medical College, Chicago, IL 60612.

Biological Cybernetics
|January 1, 1992
PubMed
Summary

Movement variability can reveal if a movement is simple and pre-planned. Analyzing time profiles of trajectory, velocity, and acceleration variability helps classify movements, confirming reaching movements as simple and pre-planned.

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Area of Science:

  • Biomechanics
  • Motor Control
  • Robotics

Background:

  • Pre-planned movements are crucial for human and robotic actions.
  • Understanding movement variability aids in classifying movement types.
  • Existing models often simplify the complex dynamics of movement variability.

Purpose of the Study:

  • To develop a model describing movement variability based on parameter variations.
  • To identify invariant properties of simple pre-planned movements.
  • To establish a method for classifying movements based on their variability time profiles.

Main Methods:

  • Developed a mathematical model for pre-planned single joint movements.
  • Analyzed trajectory, velocity, and acceleration variability using affine transformations.

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  • Derived basic functions for variability time profiles based on spatial and temporal scaling.
  • Analytically studied reaching movements and compared model predictions with experimental data.

    • Main Results:

    • Movement variability is determined by parameter variations.
    • Variability time profiles can be expanded using specific basic functions related to movement parameters.
    • The model accurately predicts the bi-modal and tri-modal nature of velocity and acceleration variance time profiles, respectively.
    • Experimental validation confirmed the model's predictions for reaching movements.

    Conclusions:

    • Reaching movements are classified as simple pre-planned movements.
    • Movement variability analysis provides an invariant property for movement classification.
    • The developed model offers a framework for attributing movements to types based on variability testing.