EMG Versus Torque Control of Human-Machine Systems: Equalizing Control Signal Variability Does not Equalize Error or Uncertainty

Reva E. Johnson, Konrad P. Kording, Levi J. Hargrove, Jonathon W. Sensinger

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

In this paper we asked the question: if we artificially raise the variability of torque control signals to match that of EMG, do subjects make similar errors and have similar uncertainty about their movements? We answered this question using two experiments in which subjects used three different control signals: torque, torque+noise, and EMG. First, we measured error on a simple target-hitting task in which subjects received visual feedback only at the end of their movements. We found that even when the signal-to-noise ratio was equal across EMG and torque+noise control signals, EMG resulted in larger errors. Second, we quantified uncertainty by measuring the just-noticeable difference of a visual perturbation. We found that for equal errors, EMG resulted in higher movement uncertainty than both torque and torque+noise. The differences suggest that performance and confidence are influenced by more than just the noisiness of the control signal, and suggest that other factors, such as the user's ability to incorporate feedback and develop accurate internal models, also have significant impacts on the performance and confidence of a person's actions. We theorize that users have difficulty distinguishing between random and systematic errors for EMG control, and future work should examine in more detail the types of errors made with EMG control.

Original languageEnglish (US)
Article number7552488
Pages (from-to)660-667
Number of pages8
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume25
Issue number6
DOIs
StatePublished - Jun 2017

Keywords

  • Electromyographic (EMG)
  • myoelectric control
  • uncertainty
  • upper limb prosthesis

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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