Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control

Janne M. Hahne, Sven Dähne, Han Jeong Hwang, Klaus Muller, Lucas C. Parra

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

Myoelectric control of a prosthetic hand with more than one degree of freedom (DoF) is challenging, and clinically available techniques require a sequential actuation of the DoFs. Simultaneous and proportional control of multiple DoFs is possible with regression-based approaches allowing for fluent and natural movements. Conventionally, the regressor is calibrated in an open-loop with training based on recorded data and the performance is evaluated subsequently. For individuals with amputation or congenital limb-deficiency who need to (re)learn how to generate suitable muscle contractions, this open-loop process may not be effective. We present a closed-loop real-time learning scheme in which both the user and the machine learn simultaneously to follow a common target. Experiments with ten able-bodied individuals show that this co-adaptive closed-loop learning strategy leads to significant performance improvements compared to a conventional open-loop training paradigm. Importantly, co-adaptive learning allowed two individuals with congenital deficiencies to perform simultaneous 2-D proportional control at levels comparable to the able-bodied individuals, despite having to a learn completely new and unfamiliar mapping from muscle activity to movement trajectories. To our knowledge, this is the first study which investigates man-machine co-adaptation for regression-based myoelectric control. The proposed training strategy has the potential to improve myographic prosthetic control in clinically relevant settings.

Original languageEnglish
Article number7038151
Pages (from-to)618-627
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume23
Issue number4
DOIs
Publication statusPublished - 2015 Jul 1
Externally publishedYes

Keywords

  • Closed-loop-control
  • co-adaptation
  • Electromyography
  • myoelectric control
  • prosthetic hand
  • real-time-learning
  • regression
  • simultaneous control

ASJC Scopus subject areas

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

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