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

32 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 - 2007
Externally publishedYes
Event2007 IEEE 10th International Conference on Rehabilitation Robotics, ICORR'07 - Noordwijk, Netherlands
Duration: Jun 12 2007Jun 15 2007

Other

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

Fingerprint

Wavelet analysis
Prosthetics
Neural networks
Grippers
Muscle
Classifiers
Electrodes
Prostheses and Implants

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] https://doi.org/10.1109/ICORR.2007.4428476

Classification of EMG signals through wavelet analysis and neural networks for controlling an active hand prosthesis. / Arvetti, Matteo; Gini, Giuseppina; Folgheraiter, Michele.

2007 IEEE 10th International Conference on Rehabilitation Robotics, ICORR'07. 2007. p. 531-536 4428476.

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

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., 4428476, pp. 531-536, 2007 IEEE 10th International Conference on Rehabilitation Robotics, ICORR'07, Noordwijk, Netherlands, 6/12/07. https://doi.org/10.1109/ICORR.2007.4428476
Arvetti M, Gini G, Folgheraiter M. 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. 2007. p. 531-536. 4428476 https://doi.org/10.1109/ICORR.2007.4428476
Arvetti, Matteo ; Gini, Giuseppina ; Folgheraiter, Michele. / Classification of EMG signals through wavelet analysis and neural networks for controlling an active hand prosthesis. 2007 IEEE 10th International Conference on Rehabilitation Robotics, ICORR'07. 2007. pp. 531-536
@inproceedings{c7bf408bf23342c9af57b6c47f91ca4f,
title = "Classification of EMG signals through wavelet analysis and neural networks for controlling an active hand prosthesis",
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.",
author = "Matteo Arvetti and Giuseppina Gini and Michele Folgheraiter",
year = "2007",
doi = "10.1109/ICORR.2007.4428476",
language = "English",
isbn = "1424413206",
pages = "531--536",
booktitle = "2007 IEEE 10th International Conference on Rehabilitation Robotics, ICORR'07",

}

TY - GEN

T1 - Classification of EMG signals through wavelet analysis and neural networks for controlling an active hand prosthesis

AU - Arvetti, Matteo

AU - Gini, Giuseppina

AU - Folgheraiter, Michele

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=48349146612&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=48349146612&partnerID=8YFLogxK

U2 - 10.1109/ICORR.2007.4428476

DO - 10.1109/ICORR.2007.4428476

M3 - Conference contribution

SN - 1424413206

SN - 9781424413201

SP - 531

EP - 536

BT - 2007 IEEE 10th International Conference on Rehabilitation Robotics, ICORR'07

ER -