A Probabilistic Analysis of Muscle Force Uncertainty for Control

Max Berniker*, Anthony Jarc, Konrad Kording, Matthew Tresch

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: We control the movements of our body and limbs through our muscles. However, the forces produced by our muscles depend unpredictably on the commands sent to them. This uncertainty has two sources: irreducible noise in the motor system's processes (i.e., motor noise) and variability in the relationship between muscle commands and muscle outputs (i.e., model uncertainty). Any controller, neural or artificial, benefits from estimating these uncertainties when choosing commands. Methods: To examine these benefits, we used an experimental preparation of the rat hindlimb to electrically stimulate muscles and measure the resulting isometric forces. We compare a functional electric stimulation (FES) controller that represents and compensates for uncertainty in muscle forces with a standard FES controller that neglects uncertainty. Results: Accounting for uncertainty substantially increased the precision of force control. Conclusion: Our study demonstrates the theoretical and practical benefits of representing muscle uncertainty when computing muscle commands. Significance: The findings are relevant beyond FES as they highlight the benefits of estimating statistical properties of muscles for both artificial controllers and the nervous system.

Original languageEnglish (US)
Article number7410020
Pages (from-to)2359-2367
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume63
Issue number11
DOIs
StatePublished - Nov 2016

Keywords

  • Biological control systems
  • computational biology
  • control design
  • force control
  • functional electrical stimulation
  • muscle force uncertainty
  • nonlinear control systems
  • open loop systems
  • optimal control

ASJC Scopus subject areas

  • Biomedical Engineering

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