Evaluation of command algorithms for control of upper-extremity neural prostheses.

Published

Journal Article

Five new command control algorithms were created to enable increased control over grasp force in upper-extremity neural prostheses. Most of these algorithms took advantage of the ability to lock or assign a steady command value to the hand neural prosthesis. Five able-bodied subjects tested the algorithms by using a shoulder controller that controlled a video-simulated hand to repeatedly complete a consistent evaluation task. A generalized estimating equations-based linear model was used to analyze the data. The algorithms were ranked via contrast analyses between the coefficient values from the linear model of the proportional control with lock algorithm, which is the algorithm presently used in neural prostheses, and each of the other algorithms. The algorithms that allowed adjustment of the command value after the hand was locked as well as algorithms that allowed a decrease in controller gain after the hand was locked performed better than the proportional control with lock algorithm. Algorithms that changed command as a function of time performed worse than the proportional control with lock algorithm. Further, the computer-based video simulator proved to be useful as a first-pass evaluation tool for neural prosthesis control.

Full Text

Duke Authors

Cited Authors

  • Humbert, SD; Snyder, SA; Grill, WM

Published Date

  • June 2002

Published In

Volume / Issue

  • 10 / 2

Start / End Page

  • 94 - 101

PubMed ID

  • 12236452

Pubmed Central ID

  • 12236452

Electronic International Standard Serial Number (EISSN)

  • 1558-0210

International Standard Serial Number (ISSN)

  • 1534-4320

Digital Object Identifier (DOI)

  • 10.1109/tnsre.2002.1031977

Language

  • eng