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Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography
The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4188712/ https://www.ncbi.nlm.nih.gov/pubmed/25289669 http://dx.doi.org/10.1371/journal.pone.0109943 |
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author | Gazzoni, Marco Celadon, Nicolò Mastrapasqua, Davide Paleari, Marco Margaria, Valentina Ariano, Paolo |
author_facet | Gazzoni, Marco Celadon, Nicolò Mastrapasqua, Davide Paleari, Marco Margaria, Valentina Ariano, Paolo |
author_sort | Gazzoni, Marco |
collection | PubMed |
description | The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements. However, the sEMG spatial distribution on the forearm during hand and finger movements and its changes due to different hand positions has never been quantified. The aim of this work is to quantify 1) the spatial localization of surface EMG activity of distinct forearm muscles during dynamic free movements of wrist and single fingers and 2) the effect of hand position on sEMG activity distribution. The subjects performed cyclic dynamic tasks involving the wrist and the fingers. The wrist tasks and the hand opening/closing task were performed with the hand in prone and neutral positions. A sensorized glove was used for kinematics recording. sEMG signals were acquired from the forearm muscles using a grid of 112 electrodes integrated into a stretchable textile sleeve. The areas of sEMG activity have been identified by a segmentation technique after a data dimensionality reduction step based on Non Negative Matrix Factorization applied to the EMG envelopes. The results show that 1) it is possible to identify distinct areas of sEMG activity on the forearm for different fingers; 2) hand position influences sEMG activity level and spatial distribution. This work gives new quantitative information about sEMG activity distribution on the forearm in healthy subjects and provides a basis for future works on the identification of optimal electrode configuration for sEMG based control of prostheses, exoskeletons, or orthoses. An example of use of this information for the optimization of the detection system for the estimation of joint kinematics from sEMG is reported. |
format | Online Article Text |
id | pubmed-4188712 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-41887122014-10-10 Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography Gazzoni, Marco Celadon, Nicolò Mastrapasqua, Davide Paleari, Marco Margaria, Valentina Ariano, Paolo PLoS One Research Article The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements. However, the sEMG spatial distribution on the forearm during hand and finger movements and its changes due to different hand positions has never been quantified. The aim of this work is to quantify 1) the spatial localization of surface EMG activity of distinct forearm muscles during dynamic free movements of wrist and single fingers and 2) the effect of hand position on sEMG activity distribution. The subjects performed cyclic dynamic tasks involving the wrist and the fingers. The wrist tasks and the hand opening/closing task were performed with the hand in prone and neutral positions. A sensorized glove was used for kinematics recording. sEMG signals were acquired from the forearm muscles using a grid of 112 electrodes integrated into a stretchable textile sleeve. The areas of sEMG activity have been identified by a segmentation technique after a data dimensionality reduction step based on Non Negative Matrix Factorization applied to the EMG envelopes. The results show that 1) it is possible to identify distinct areas of sEMG activity on the forearm for different fingers; 2) hand position influences sEMG activity level and spatial distribution. This work gives new quantitative information about sEMG activity distribution on the forearm in healthy subjects and provides a basis for future works on the identification of optimal electrode configuration for sEMG based control of prostheses, exoskeletons, or orthoses. An example of use of this information for the optimization of the detection system for the estimation of joint kinematics from sEMG is reported. Public Library of Science 2014-10-07 /pmc/articles/PMC4188712/ /pubmed/25289669 http://dx.doi.org/10.1371/journal.pone.0109943 Text en © 2014 Gazzoni et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Gazzoni, Marco Celadon, Nicolò Mastrapasqua, Davide Paleari, Marco Margaria, Valentina Ariano, Paolo Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography |
title | Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography |
title_full | Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography |
title_fullStr | Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography |
title_full_unstemmed | Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography |
title_short | Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography |
title_sort | quantifying forearm muscle activity during wrist and finger movements by means of multi-channel electromyography |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4188712/ https://www.ncbi.nlm.nih.gov/pubmed/25289669 http://dx.doi.org/10.1371/journal.pone.0109943 |
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