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Stabilization Strategies for Unstable Dynamics

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  • Devjani J Saha
  • Pietro Morasso

Abstract

Background: When humans are faced with an unstable task, two different stabilization mechanisms are possible: a high-stiffness strategy, based on the inherent elastic properties of muscles/tools/manipulated objects, or a low-stiffness strategy, based on an explicit positional feedback mechanism. Specific constraints related to the dynamics of the task and/or the neuromuscular system often force people to adopt one of these two strategies. Methodology/Findings: This experiment was designed such that subjects could achieve stability using either strategy, with a marked difference in terms of effort and control requirements between the two strategies. The task was to balance a virtual mass in an unstable environment via two elastic linkages that connected the mass to each hand. The dynamics of the mass under the influence of the unstable force field and the forces applied through the linkages were simulated using a bimanual, planar robot. The two linkages were non-linear, with a stiffness that increased with the amount of stretch. The mass could be stabilized by stretching the linkages to achieve a stiffness that was greater than the instability coefficient of the unstable field (high-stiffness), or by balancing the mass with sequences of small force impulses (low-stiffness). The results showed that 62% of the subjects quickly adopted the high-stiffness strategy, with stiffness ellipses that were aligned along the direction of instability. The remaining subjects applied the low-stiffness strategy, with no clear preference for the orientation of the stiffness ellipse. Conclusions: The choice of a strategy was based on the bimanual coordination of the hands: high-stiffness subjects achieved stability quickly by separating the hands to stretch the linkages, while the low-stiffness subjects kept the hands close together and took longer to achieve stability but with lower effort. We suggest that the existence of multiple solutions leads to different types of skilled behavior in unstable environments.

Suggested Citation

  • Devjani J Saha & Pietro Morasso, 2012. "Stabilization Strategies for Unstable Dynamics," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-13, January.
    • Handle: RePEc:plo:pone00:0030301
    DOI: 10.1371/journal.pone.0030301
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    References listed on IDEAS

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    1. Yoshiyuki Asai & Yuichi Tasaka & Kunihiko Nomura & Taishin Nomura & Maura Casadio & Pietro Morasso, 2009. "A Model of Postural Control in Quiet Standing: Robust Compensation of Delay-Induced Instability Using Intermittent Activation of Feedback Control," PLOS ONE, Public Library of Science, vol. 4(7), pages 1-14, July.
    2. Etienne Burdet & Rieko Osu & David W. Franklin & Theodore E. Milner & Mitsuo Kawato, 2001. "The central nervous system stabilizes unstable dynamics by learning optimal impedance," Nature, Nature, vol. 414(6862), pages 446-449, November.
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    1. Jacopo Zenzeri & Dalia De Santis & Pietro Morasso, 2014. "Strategy Switching in the Stabilization of Unstable Dynamics," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-24, June.

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