High-Density Electromyography and Motor Skill Learning for Robust Long-Term Control of a 7-DoF Robot Arm

Mark Ison, Ivan Vujaklija, Bryan Whitsell, Dario Farina, Panagiotis Artemiadis

Research output: Contribution to journalArticle

49 Scopus citations

Abstract

Myoelectric control offers a direct interface between human intent and various robotic applications through recorded muscle activity. Traditional control schemes realize this interface through direct mapping or pattern recognition techniques. The former approach provides reliable control at the expense of functionality, while the latter increases functionality at the expense of long-term reliability. An alternative approach, using concepts of motor learning, provides session-independent simultaneous control, but previously relied on consistent electrode placement over biomechanically independent muscles. This paper extends the functionality and practicality of the motor learning-based approach, using high-density electrode grids and muscle synergy-inspired decomposition to generate control inputs with reduced constraints on electrode placement. The method is demonstrated via real-time simultaneous and proportional control of a 4-DoF myoelectric interface over multiple days. Subjects showed learning trends consistent with typical motor skill learning without requiring any retraining or recalibration between sessions. Moreover, they adjusted to physical constraints of a robot arm after learning the control in a constraint-free virtual interface, demonstrating robust control as they performed precision tasks. The results demonstrate the efficacy of the proposed man-machine interface as a viable alternative to conventional control schemes for myoelectric interfaces designed for long-term use.

Original languageEnglish (US)
Article number7073629
Pages (from-to)424-433
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume24
Issue number4
DOIs
StatePublished - Apr 1 2016

Keywords

  • Electromyography (EMG)
  • high-density electromyography (EMG)
  • human-robot interaction
  • motor learning
  • myoelectric control
  • prosthetics
  • real-time systems
  • simultaneous control

ASJC Scopus subject areas

  • Neuroscience(all)
  • Computer Science Applications
  • Biomedical Engineering

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