Feasibility of EMG-based control of shoulder muscle FNS via artificial neural network

R. F. Kirsch*, P. P. Parikh, A. M. Acosta, F. C T Van Der Helm

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

We investigated the potential use of EMG recordings from voluntary shoulder muscles in individuals with C5 spinal cord injury to automatically control the stimulation to paralyzed shoulder muscles in a task-appropriate manner. A musculoskeletal model of the human shoulder and elbow was modified to have maximum muscle forces appropriate for C5 spinal cord injury, including completely and partially paralyzed muscles. Inverse model simulations generated muscle activation levels that were used to train an artificial neural network (ANN) to automatically generate appropriate stimulation patterns for the "paralyzed" muscles based on "voluntary" muscle activations. We found that substantial additional shoulder strength could be provided by assuming that just two paralyzed muscles (pectoralis major and latissimus dorsi) were stimulated. Further, the needed activations of these "stimulated" muscles could be predicted with reasonable accuracy using the activation levels just two "voluntary" muscles (trapezius and rhomboids) as ANN inputs.

Original languageEnglish (US)
Pages (from-to)1293-1296
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume2
StatePublished - Dec 1 2001
Event23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Istanbul, Turkey
Duration: Oct 25 2001Oct 28 2001

Keywords

  • ANN
  • Artificial neural network
  • EMG
  • FES
  • FNS
  • Musculoskeletal model
  • Paralysis
  • Shoulder
  • Spinal cord injury

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Fingerprint Dive into the research topics of 'Feasibility of EMG-based control of shoulder muscle FNS via artificial neural network'. Together they form a unique fingerprint.

Cite this