Negative viscosity can enhance learning of inertial dynamics

Felix C. Huang, James L. Patton, Ferdinando A. Mussa-Ivaldil

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

We investigated how learning of inertial load manipulation is influenced by movement amplification with negative viscosity. Using a force-feedback device, subjects trained on anisotropic loads (5 orientations) with free movements in one of three conditions (inertia only, negative viscosity only, or combined), prior to common evaluation conditions (prescribed circular pattern with inertia only). Training with Combined-Load resulted in lower error (6.89±3.25%) compared to Inertia-Only (8.40±4.32%) and Viscosity-Only (8.17±4.13%) according to radial deviation analysis (% of trial mean radius). Combined-Load and Inertia- Only groups exhibited similar unexpected no-load trials (8.38±4.31% versns 8.91±4.70% of trial mean radius), which suggests comparable low-impedance strategies. These findings are remarkable since negative viscosity, only available during training, evidently enhanced learning when combined with inertia. Modeling analysis suggests that a feedforward afte-reffect of negative viscosity cannot predict such performance gains. Instead, results from Combined-Load training are consistent with greater feedforward inertia compensation along with a small increase in impedance control. The capability of the nervous system to generalize learning from negative viscosity suggests an intriguing new method for enhancing sensorimotor adaptation.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Rehabilitation Robotics, ICORR 2009
Pages474-479
Number of pages6
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Rehabilitation Robotics, ICORR 2009 - Kyoto, Japan
Duration: Jun 23 2009Jun 26 2009

Publication series

Name2009 IEEE International Conference on Rehabilitation Robotics, ICORR 2009

Other

Other2009 IEEE International Conference on Rehabilitation Robotics, ICORR 2009
Country/TerritoryJapan
CityKyoto
Period6/23/096/26/09

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition

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