Adaptive neural network control of cyclic movements using functional neuromuscular stimulation

Jo Anne Riess, James J. Abbas

Research output: Contribution to journalArticle

65 Scopus citations

Abstract

In this study, we evaluated the performance of an adaptive feedforward controller and its ability to automatically develop and customize stimulation patterns for use in functional neuromuscular stimulation (FNS) systems. Results from previous experiments using the pattern generator/pattern shaper (PG/PS) controller to generate isometric contractions demonstrated its ability to adjust stimulation patterns to account for recruitment nonlinearities and muscle dynamics. In this study, the PG/PS controller was tested under isotonic conditions. This evaluation required the PG/PS controller to account: for muscle length-tension and force-velocity properties as well as limb dynamics. The performance of the adaptive controller was also compared with that of a proportional-derivative (PD) feedback controller. The PG/PS controller is composed of a neural network system that adaptively filters a periodic signal to produce a muscle stimulation pattern for generating cyclic movements. We used computer- simulated models to determine controller parameters for the PG/PS and PD controller that perform well across a variety of musculoskeletal systems. The controllers were then experimentally evaluated on both legs of two subjects with spinal cord injury. Results indicated that the PG/PS controller was able to achieve and maintain better tracking performance than the PD controller. This study indicates that the PG/PS control system may provide an effective mechanism for automatically customizing stimulation patterns for individuals using FNS systems.

Original languageEnglish (US)
Pages (from-to)42-52
Number of pages11
JournalIEEE Transactions on Rehabilitation Engineering
Volume8
Issue number1
DOIs
StatePublished - May 10 2000
Externally publishedYes

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Keywords

  • Adaptive control
  • Cyclic movement
  • Functional neuromuscular stimulation (FNS)
  • Neural network
  • Paraplegia

ASJC Scopus subject areas

  • Engineering(all)

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