Classification of muscle twitch response using ANN: Application in multi-pad electrode optimization

Nebojša Malešević, Lana Popović, Goran Bijelić, Goran Kvaščev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

In this paper we present a method for optimization of spatial selectivity of multi-pad electrode during transcutaneous Functional Electrical Stimulation (FES). The presented method is based on measurent of individual muscle twitches using Micro-Electro-Mechanical Systems (MEMS) accelerometers positioned on hand, while stimulating with low frequency electrical stimulation via pads within multi-pad electrode. When elicited, wrist or fingers flexion/extension produce different, characteristic wave shapes of acceleration, by using trained Artificial Neural Network (ANN) we can detect these characteristic signals and detect correlation of each pad and activated muscle beneath. Results presented in this paper show high degree of accurate classification of the elicited movement in inter-subject testing.

Original languageEnglish
Title of host publication10th Symposium on Neural Network Applications in Electrical Engineering, NEUREL-2010 - Proceedings
Pages11-13
Number of pages3
DOIs
Publication statusPublished - 2010
Event10th Symposium on Neural Network Applications in Electrical Engineering, NEUREL-2010 - Belgrade, Serbia
Duration: 23 Sept 201025 Sept 2010

Publication series

Name10th Symposium on Neural Network Applications in Electrical Engineering, NEUREL-2010 - Proceedings

Conference

Conference10th Symposium on Neural Network Applications in Electrical Engineering, NEUREL-2010
Country/TerritorySerbia
CityBelgrade
Period23/09/1025/09/10

Keywords

  • FES
  • Multi-pad electrode
  • Neural network
  • Selectivity

Fingerprint

Dive into the research topics of 'Classification of muscle twitch response using ANN: Application in multi-pad electrode optimization'. Together they form a unique fingerprint.

Cite this