Functional electrical stimulation controlled by artificial neural networks: Pilot experiments with simple movements are promising for rehabilitation applications

Simona Ferrante, Alessandra Pedrocchi, Marco Iannò, Elena De Momi, Maurizio Ferrarin, Giancarlo Ferrigno

Research output: Contribution to journalArticle

Abstract

This study falls within the ambit of research on functional electrical stimulation for the design of rehabilitation training for spinal cord injured patients. In this context, a crucial issue is the control of the stimulation parameters in order to optimize the patterns of muscle activation and to increase the duration of the exercises. An adaptive control system (NEURADAPT) based on artificial neural networks (ANNs) was developed to control the knee joint in accordance with desired trajectories by stimulating quadriceps muscles. This strategy includes an inverse neural model of the stimulated limb in the feedforward line and a neural network trained on-line in the feedback loop. NEURADAPT was compared with a linear closed-loop proportional integrative derivative (PID) controller and with a model-based neural controller (NEUROPID). Experiments on two subjects (one healthy and one paraplegic) show the good performance of NEURADAPT, which is able to reduce the time lag introduced by the PID controller. In addition, control systems based on ANN techniques do not require complicated calibration procedures at the beginning of each experimental session. After the initial learning phase, the ANN, thanks to its generalization capacity, is able to cope with a certain range of variability of skeletal muscle properties.

Original languageEnglish
Pages (from-to)243-252
Number of pages10
JournalFunctional Neurology
Volume19
Issue number4
Publication statusPublished - Oct 2004

Keywords

  • Artificial neural networks
  • Functional electrical stimulation
  • Non-linear adaptive control systems
  • Rehabilitation engineering

ASJC Scopus subject areas

  • Clinical Neurology
  • Neuroscience(all)

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