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Artificial Joint Speed Feedback for Myoelectric Prosthesis Controls

View ORCID ProfileEric J. Earley, Reva E. Johnson, Jonathon W. Sensinger, Levi J. Hargrove
doi: https://doi.org/10.1101/2020.11.17.385450
Eric J. Earley
1Department of Biomedical Engineering, Northwestern University, Chicago, IL, USA
2Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, USA
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  • ORCID record for Eric J. Earley
  • For correspondence: ericearley@u.northwestern.edu
Reva E. Johnson
3Department of Mechanical Engineering and Bioengineering, Valparaiso University, Valparaiso, IN, USA
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Jonathon W. Sensinger
4Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, CA
5Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB, CA
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Levi J. Hargrove
1Department of Biomedical Engineering, Northwestern University, Chicago, IL, USA
2Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, USA
6Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA
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I. Abstract

Accurate control of human limbs involves both feedforward and feedback signals. For prosthetic arms, feedforward control is commonly accomplished by recording myoelectric signals from the residual limb to predict the user’s intent, but augmented feedback signals are not explicitly provided in commercial devices. Previous studies have demonstrated inconsistent results when artificial feedback was provided in the presence of vision. We hypothesized that negligible benefits in past studies may have been due to artificial feedback with low precision compared to vision, which results in heavy reliance on vision during reaching tasks. Furthermore, we anticipated more reliable benefits from artificial feedback when providing information that vision estimates with high uncertainty – joint speed. In this study, we test an artificial sensory feedback system providing joint speed information and how it impacts performance and adaptation during a hybrid positional-and-myoelectric ballistic reaching task. We found modest improvement in overall reaching errors after perturbed control, and that high prosthesis control noise was compensated for by strategic overreaching with the positional control and underreaching with the myoelectric control. These results provide insights into the relevant factors influencing the improvements conferred by artificial sensory feedback.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://osf.io/v7cu2/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 20, 2020.
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Artificial Joint Speed Feedback for Myoelectric Prosthesis Controls
Eric J. Earley, Reva E. Johnson, Jonathon W. Sensinger, Levi J. Hargrove
bioRxiv 2020.11.17.385450; doi: https://doi.org/10.1101/2020.11.17.385450
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Artificial Joint Speed Feedback for Myoelectric Prosthesis Controls
Eric J. Earley, Reva E. Johnson, Jonathon W. Sensinger, Levi J. Hargrove
bioRxiv 2020.11.17.385450; doi: https://doi.org/10.1101/2020.11.17.385450

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