Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Research on Biomedical Engineering (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000300202 |
Resumo: | Abstract Introduction Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi’s fractal dimension) combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM), K-nearest neighbors (KNN) and linear discriminant analysis (LDA) were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). Results The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees. |
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Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputeesElectromyographyUpper-limb prosthesisLow-density surface electromyographyDexterous hand gesturesPattern recognitionAbstract Introduction Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi’s fractal dimension) combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM), K-nearest neighbors (KNN) and linear discriminant analysis (LDA) were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). Results The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.Sociedade Brasileira de Engenharia Biomédica2017-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000300202Research on Biomedical Engineering v.33 n.3 2017reponame:Research on Biomedical Engineering (Online)instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)instacron:SBEB10.1590/2446-4740.08516info:eu-repo/semantics/openAccessMayor,John Jairo VillarejoCosta,Regina MamedeFrizera Neto,AnselmoBastos,Teodiano Freireeng2018-08-02T00:00:00Zoai:scielo:S2446-47402017000300202Revistahttp://www.rbejournal.org/https://old.scielo.br/oai/scielo-oai.php||rbe@rbejournal.org2446-47402446-4732opendoar:2018-08-02T00:00Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)false |
dc.title.none.fl_str_mv |
Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees |
title |
Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees |
spellingShingle |
Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees Mayor,John Jairo Villarejo Electromyography Upper-limb prosthesis Low-density surface electromyography Dexterous hand gestures Pattern recognition |
title_short |
Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees |
title_full |
Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees |
title_fullStr |
Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees |
title_full_unstemmed |
Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees |
title_sort |
Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees |
author |
Mayor,John Jairo Villarejo |
author_facet |
Mayor,John Jairo Villarejo Costa,Regina Mamede Frizera Neto,Anselmo Bastos,Teodiano Freire |
author_role |
author |
author2 |
Costa,Regina Mamede Frizera Neto,Anselmo Bastos,Teodiano Freire |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Mayor,John Jairo Villarejo Costa,Regina Mamede Frizera Neto,Anselmo Bastos,Teodiano Freire |
dc.subject.por.fl_str_mv |
Electromyography Upper-limb prosthesis Low-density surface electromyography Dexterous hand gestures Pattern recognition |
topic |
Electromyography Upper-limb prosthesis Low-density surface electromyography Dexterous hand gestures Pattern recognition |
description |
Abstract Introduction Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi’s fractal dimension) combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM), K-nearest neighbors (KNN) and linear discriminant analysis (LDA) were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). Results The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000300202 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000300202 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2446-4740.08516 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Engenharia Biomédica |
publisher.none.fl_str_mv |
Sociedade Brasileira de Engenharia Biomédica |
dc.source.none.fl_str_mv |
Research on Biomedical Engineering v.33 n.3 2017 reponame:Research on Biomedical Engineering (Online) instname:Sociedade Brasileira de Engenharia Biomédica (SBEB) instacron:SBEB |
instname_str |
Sociedade Brasileira de Engenharia Biomédica (SBEB) |
instacron_str |
SBEB |
institution |
SBEB |
reponame_str |
Research on Biomedical Engineering (Online) |
collection |
Research on Biomedical Engineering (Online) |
repository.name.fl_str_mv |
Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB) |
repository.mail.fl_str_mv |
||rbe@rbejournal.org |
_version_ |
1752126288748871680 |