Classification of EMG signals through wavelet analysis and neural networks for controlling an active hand prosthesis

Matteo Arvetti, Giuseppina Gini, Michele Folgheraiter

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

33 Citations (Scopus)

Abstract

In order to increase the effectiveness of active hand prostheses we intend to exploit electromyographic (EMG) signals more than in the usual application for controlling one degree of freedom (gripper open or closed). Among all the numerous muscles that move the fingers, we chose only the ones in the forearm, to have a simple way to position only two electrodes. We analyze the EMG signals coming from two different subjects using a novel integration of ANN and wavelet, We show how to discriminate between more movements, five in this study, using our new classifier. Results show how the methodology we adopted allows us to obtain good accuracy in classifying the hand postures, and opens the way to more functional hand prostheses.

Original languageEnglish
Title of host publication2007 IEEE 10th International Conference on Rehabilitation Robotics, ICORR'07
Pages531-536
Number of pages6
DOIs
Publication statusPublished - Dec 1 2007
Event2007 IEEE 10th International Conference on Rehabilitation Robotics, ICORR'07 - Noordwijk, Netherlands
Duration: Jun 12 2007Jun 15 2007

Publication series

Name2007 IEEE 10th International Conference on Rehabilitation Robotics, ICORR'07

Other

Other2007 IEEE 10th International Conference on Rehabilitation Robotics, ICORR'07
CountryNetherlands
CityNoordwijk
Period6/12/076/15/07

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Arvetti, M., Gini, G., & Folgheraiter, M. (2007). Classification of EMG signals through wavelet analysis and neural networks for controlling an active hand prosthesis. In 2007 IEEE 10th International Conference on Rehabilitation Robotics, ICORR'07 (pp. 531-536). [4428476] (2007 IEEE 10th International Conference on Rehabilitation Robotics, ICORR'07). https://doi.org/10.1109/ICORR.2007.4428476