Acquisition and analysis of EMG signals to recognize multiple hand movements for prosthetic applications

Giuseppina Gini, Matteo Arvetti, Ian Somlai, Michele Folgheraiter

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

One of the main problems in developing active prosthesis is how to control them in a natural way. In order to increase the effectiveness of hand prostheses there is a need in better exploiting electromyography (EMG) signals. After an analysis of the movements necessary for grasping, we individuated five movements for the wrist-hand mobility. Then we designed the basic electronics and software for the acquisition and the analysis of the EMG signals. We built a small size electronic device capable of registering them that can be integrated into a hand prosthesis. Among all the numerous muscles that move the fingers, we have chosen the ones in the forearm and positioned only two electrodes. To recognize the operation, we developed a classification system, using a novel integration of Artificial Neural Networks (ANN) and wavelet features.

Original languageEnglish
Pages (from-to)145-155
Number of pages11
JournalApplied Bionics and Biomechanics
Volume9
Issue number2
DOIs
Publication statusPublished - 2012

Keywords

  • EMG signals
  • neural networks
  • pattern recognition
  • wavelet network

ASJC Scopus subject areas

  • Biotechnology
  • Medicine (miscellaneous)
  • Bioengineering
  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Acquisition and analysis of EMG signals to recognize multiple hand movements for prosthetic applications'. Together they form a unique fingerprint.

  • Cite this