User performance with a transradial multi-articulating hand prosthesis during pattern recognition and direct control home use

Ann M. Simon, Kristi L. Turner, Laura A. Miller, Benjamin K. Potter, Mark D. Beachler, Gregory A. Dumanian, Levi J. Hargrove, Todd A. Kuiken

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

With the increasing availability of more advanced prostheses individuals with a transradial amputation can now be fit with single to multi-degree of freedom hands. Reliable and accurate control of these multi-grip hands still remains challenging. This is the first multi-user study to investigate at-home control and use of a multi-grip hand prosthesis under pattern recognition and direct control. Individuals with a transradial amputation were fitted with and trained to use an OSSUR i-Limb Ultra Revolution with Coapt COMPLETE CONTROL system. They participated in two 8-week home trials using the hand under myoelectric direct and pattern recognition control in a randomized order. While at home, participants demonstrated broader usage of grips in pattern recognition compared to direct control. After the home trial, they showed significant improvements in the Assessment of Capacity for Myoelectric Control (ACMC) outcome measure while using pattern recognition control compared to direct control; other outcome measures showed no differences between control styles. Additionally, this study provided a unique opportunity to evaluate EMG signals during home use. Offline analysis of calibration data showed that users were 81.5% [7.1] accurate across a range of three to five grips. Although EMG signal noise was identified during some calibrations, overall EMG quality was sufficient to provide users with control performance at or better than direct control.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
DOIs
StateAccepted/In press - 2022

Keywords

  • Control systems
  • Electromyography
  • Pattern recognition
  • Prosthetics
  • Switches
  • Testing
  • Wrist
  • below-elbow amputation
  • home use
  • machine learning
  • myoelectric control
  • prosthesis function

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering
  • Rehabilitation

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