Cost-effective system for the classification of muscular intent using surface electromyography and artificial neural networks

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

4 Citations (Scopus)

Abstract

This paper presents the implementation of a system to classify muscular intent. A neural network is used for this purpose. After skin preparation, feature extraction, network training and real-time testing, an average overall classification accuracy of 93.3% over three possible gestures was obtained. Ultimately, the results obtained speak to the suitability of an Arduino-based system for the acquisition and decoding of muscular intent. This result is indicative of the potential of the Arduino microcontroller in this application, to provide effective performance at a far lower price-point than its competition.

Original languageEnglish
Title of host publication2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)
PublisherIEEE
Pages366-371
Number of pages6
ISBN (Electronic)9781509056866
ISBN (Print)9781509056859
DOIs
Publication statusPublished - 18 Dec 2017

Keywords

  • Arduino
  • Cost-Effective
  • Electromyography
  • Muscular Intent
  • Neural Network
  • Orthosis
  • Prosthesis

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Instrumentation
  • Safety, Risk, Reliability and Quality

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