A decision-based velocity ramp for minimizing the effect of misclassifications during real-time pattern recognition control

Ann M. Simon*, Levi J. Hargrove, Blair A. Lock, Todd A. Kuiken

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

84 Scopus citations

Abstract

Real-time pattern recognition control is frequently affected by misclassifications. This study investigated the use of a decision-based velocity ramp that attenuated movement speed after a change in classifier decision. The goal was to improve prosthesis positioning by minimizing the effect of unintended movements. Nonamputee and amputee subjects controlled a prosthesis in real time using pattern recognition. While performing a target achievement test in a virtual environment, subjects had a significantly higher completion rate ( p< 0.05) and a more direct path (p < 0.05) to the target with the velocity ramp than without it. Using a physical prosthesis, subjects stacked a greater average number of 1-in cubes (p< 0.05) in 3 min with the velocity ramp than without it (76 more blocks for nonamputees; 89 more blocks for amputees). Real-time control using the velocity ramp also showed significant performance improvements above using majority vote. Eighty-three percent of subjects preferred to control the prosthesis using the velocity ramp. These results suggest that using a decision-based velocity ramp with pattern recognition may improve user performance. Since the velocity ramp is a postprocessing step, it has the potential to be used with a variety of classifiers for many applications.

Original languageEnglish (US)
Article number5768069
Pages (from-to)2360-2368
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume58
Issue number8
DOIs
StatePublished - Aug 2011

Keywords

  • Myoelectric control
  • pattern recognition
  • prosthesis
  • surface electromyography (EMG)
  • upper limb

ASJC Scopus subject areas

  • Biomedical Engineering

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