A Neuro-Fuzzy System for Characterization of Arm Movements

The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to a...

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Main Authors: Alexandre Balbinot, Gabriela Favieiro
Format: Article
Language:English
Published: MDPI AG 2013-02-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/13/2/2613
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author Alexandre Balbinot
Gabriela Favieiro
author_facet Alexandre Balbinot
Gabriela Favieiro
author_sort Alexandre Balbinot
collection DOAJ
description The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours).
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spelling doaj.art-5303033a65c74319942d61847ba8d03f2022-12-22T02:56:49ZengMDPI AGSensors1424-82202013-02-011322613263010.3390/s130202613A Neuro-Fuzzy System for Characterization of Arm MovementsAlexandre BalbinotGabriela FavieiroThe myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours).http://www.mdpi.com/1424-8220/13/2/2613biomedical instrumentationsurface electromyography (sEMG)arm movementsneuro-fuzzy system
spellingShingle Alexandre Balbinot
Gabriela Favieiro
A Neuro-Fuzzy System for Characterization of Arm Movements
Sensors
biomedical instrumentation
surface electromyography (sEMG)
arm movements
neuro-fuzzy system
title A Neuro-Fuzzy System for Characterization of Arm Movements
title_full A Neuro-Fuzzy System for Characterization of Arm Movements
title_fullStr A Neuro-Fuzzy System for Characterization of Arm Movements
title_full_unstemmed A Neuro-Fuzzy System for Characterization of Arm Movements
title_short A Neuro-Fuzzy System for Characterization of Arm Movements
title_sort neuro fuzzy system for characterization of arm movements
topic biomedical instrumentation
surface electromyography (sEMG)
arm movements
neuro-fuzzy system
url http://www.mdpi.com/1424-8220/13/2/2613
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