Stability and motor adaptation in human arm movements

  • Authors:

  • E. Burdet;P. Tee;I. Mareels;E. Milner;M. Chew;W. Franklin;R. Osu;M. Kawato

  • Affiliations:

  • Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore, Singapore and Department of Bioengineering, Imperial College London, 10 Kent Ridg ...;Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore, Singapore;Department of Electrical and Electronic Engineering, The University of Melbourne, 10 Kent Ridge Crescent, 119260, Melbourne, Australia;School of Kinesiology, Simon Fraser University, 10 Kent Ridge Crescent, 119260, Burnaby, British Columbia, Canada;Department of Electrical and Electronic Engineering, The University of Melbourne, 10 Kent Ridge Crescent, 119260, Melbourne, Australia;ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai “Keihanna Science city”, 619-0288, Kyoto, British Columbia, Japan;ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai “Keihanna Science city”, 619-0288, Kyoto, British Columbia, Japan;ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai “Keihanna Science city”, 619-0288, Kyoto, British Columbia, Japan

  • Venue:
  • Biological Cybernetics
  • Year:
  • 2005

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Abstract

In control, stability captures the reproducibility of motions and the robustness to environmental and internal perturbations. This paper examines how stability can be evaluated in human movements, and possible mechanisms by which humans ensure stability. First, a measure of stability is introduced, which is simple to apply to human movements and corresponds to Lyapunov exponents. Its application to real data shows that it is able to distinguish effectively between stable and unstable dynamics. A computational model is then used to investigate stability in human arm movements, which takes into account motor output variability and computes the force to perform a task according to an inverse dynamics model. Simulation results suggest that even a large time delay does not affect movement stability as long as the reflex feedback is small relative to muscle elasticity. Simulations are also used to demonstrate that existing learning schemes, using a monotonic antisymmetric update law, cannot compensate for unstable dynamics. An impedance compensation algorithm is introduced to learn unstable dynamics, which produces similar adaptation responses to those found in experiments.