Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison.

Journal: Journal of neuroengineering and rehabilitation
Published Date:

Abstract

BACKGROUND: Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm's output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions.

Authors

  • Michael D Paskett
    Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 84112, USA. michael.paskett@utah.edu.
  • Mark R Brinton
    School of Engineering, Math and Computer Science, Elizabethtown College, Elizabethtown, PA, 17022, USA.
  • Taylor C Hansen
    Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
  • Jacob A George
    Center for Clinical and Translational Science, University of Utah, Salt Lake City, UT, 84112, USA.
  • Tyler S Davis
  • Christopher C Duncan
    Division of Physical Medicine and Rehabilitation, University of Utah, Salt Lake City, UT, 84112, USA.
  • Gregory A Clark