Application of optimization methods to predict performance of a vibrotactile balance prosthesis

Adam D. Goodworth, Conrad Wall, Robert J. Peterka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

System Identification and modeling methods were employed to investigate how subjects use orientation information provided by a vibrotactile balance prosthesis. Previous results showed systematic, frequency-dependent changes in the dynamic responses to postural perturbations due to surface tilts as a function of prosthesis feedback parameters. These results could be modeled by a relatively simple feedback control model with the contribution from the prosthesis feedback being dependent on the relative proportion of sway position and velocity information encoded by the prosthesis. Results presented here identify candidate "cost functions" that predict this dependency on prosthesis feedback information. The most accurate prediction was obtained from a cost function that included a weighted combination of the root-mean-square values of body sway jerk (3rd derivative of angular body sway) and angular body sway.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
Pages510-513
Number of pages4
DOIs
StatePublished - Sep 25 2007
Event3rd International IEEE EMBS Conference on Neural Engineering - Kohala Coast, HI, United States
Duration: May 2 2007May 5 2007

Publication series

NameProceedings of the 3rd International IEEE EMBS Conference on Neural Engineering

Other

Other3rd International IEEE EMBS Conference on Neural Engineering
CountryUnited States
CityKohala Coast, HI
Period5/2/075/5/07

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ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Neuroscience (miscellaneous)

Cite this

Goodworth, A. D., Wall, C., & Peterka, R. J. (2007). Application of optimization methods to predict performance of a vibrotactile balance prosthesis. In Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering (pp. 510-513). [4227326] (Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering). https://doi.org/10.1109/CNE.2007.369721