High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans
This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated...
- Autores:
-
Rojas-Martínez, Mónica
Serna Higuita, Leidy Yanet
Jordanic, Mislav
Marateb, Hamid Reza
Merletti, Roberto
Mañanas, Miguel Angel
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Universidad El Bosque
- Repositorio:
- Repositorio U. El Bosque
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unbosque.edu.co:20.500.12495/5181
- Acceso en línea:
- http://hdl.handle.net/20.500.12495/5181
https://doi.org/10.6084/m9.figshare.12808307
- Palabra clave:
- Electromiografía
Fatiga muscular
Contracción isométrica
- Rights
- openAccess
- License
- Attribution 4.0 International
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dc.title.spa.fl_str_mv |
High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans |
dc.title.translated.spa.fl_str_mv |
High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans |
title |
High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans |
spellingShingle |
High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans Electromiografía Fatiga muscular Contracción isométrica |
title_short |
High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans |
title_full |
High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans |
title_fullStr |
High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans |
title_full_unstemmed |
High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans |
title_sort |
High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans |
dc.creator.fl_str_mv |
Rojas-Martínez, Mónica Serna Higuita, Leidy Yanet Jordanic, Mislav Marateb, Hamid Reza Merletti, Roberto Mañanas, Miguel Angel |
dc.contributor.author.none.fl_str_mv |
Rojas-Martínez, Mónica Serna Higuita, Leidy Yanet Jordanic, Mislav Marateb, Hamid Reza Merletti, Roberto Mañanas, Miguel Angel |
dc.subject.decs.spa.fl_str_mv |
Electromiografía Fatiga muscular Contracción isométrica |
topic |
Electromiografía Fatiga muscular Contracción isométrica |
description |
This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from five upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classification methods. About 6% of the total number of signals were identified as “bad” channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identification of effort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identification among other uses. |
publishDate |
2020 |
dc.date.accessioned.none.fl_str_mv |
2020-12-07T16:32:23Z |
dc.date.available.none.fl_str_mv |
2020-12-07T16:32:23Z |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.local.none.fl_str_mv |
Artículo de revista |
dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
format |
http://purl.org/coar/resource_type/c_6501 |
dc.identifier.issn.none.fl_str_mv |
2052-4463 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12495/5181 |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.6084/m9.figshare.12808307 |
dc.identifier.instname.spa.fl_str_mv |
instname:Universidad El Bosque |
dc.identifier.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional Universidad El Bosque |
dc.identifier.repourl.none.fl_str_mv |
repourl:https://repositorio.unbosque.edu.co |
identifier_str_mv |
2052-4463 instname:Universidad El Bosque reponame:Repositorio Institucional Universidad El Bosque repourl:https://repositorio.unbosque.edu.co |
url |
http://hdl.handle.net/20.500.12495/5181 https://doi.org/10.6084/m9.figshare.12808307 |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofseries.spa.fl_str_mv |
Scientific data, 2052-4463, Vol. 7, Nro. 1, 2020 |
dc.relation.uri.none.fl_str_mv |
https://www.nature.com/articles/s41597-020-00717-6 |
dc.rights.*.fl_str_mv |
Attribution 4.0 International |
dc.rights.uri.*.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
dc.rights.local.spa.fl_str_mv |
Acceso abierto |
dc.rights.accessrights.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess Acceso abierto |
dc.rights.creativecommons.none.fl_str_mv |
2020-11-16 |
rights_invalid_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ Acceso abierto http://purl.org/coar/access_right/c_abf2 2020-11-16 |
eu_rights_str_mv |
openAccess |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
Springer Nature |
dc.publisher.journal.spa.fl_str_mv |
Scientific data |
institution |
Universidad El Bosque |
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Rojas-Martínez, MónicaSerna Higuita, Leidy YanetJordanic, MislavMarateb, Hamid RezaMerletti, RobertoMañanas, Miguel Angel2020-12-07T16:32:23Z2020-12-07T16:32:23Z2052-4463http://hdl.handle.net/20.500.12495/5181https://doi.org/10.6084/m9.figshare.12808307instname:Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquerepourl:https://repositorio.unbosque.edu.coapplication/pdfengSpringer NatureScientific dataScientific data, 2052-4463, Vol. 7, Nro. 1, 2020https://www.nature.com/articles/s41597-020-00717-6Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Acceso abiertohttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessAcceso abierto2020-11-16High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humansHigh-density surface electromyography signals during isometric contractions of elbow muscles of healthy humansArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85ElectromiografíaFatiga muscularContracción isométricaThis paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from five upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classification methods. About 6% of the total number of signals were identified as “bad” channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identification of effort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identification among other uses.ORIGINALMónica Rojas-Martínez, Leidy Yanet Serna, Mislav Jordanic_2020.pdfMónica Rojas-Martínez, Leidy Yanet Serna, Mislav Jordanic_2020.pdfapplication/pdf2889002https://repositorio.unbosque.edu.co/bitstreams/d92d8ca6-2954-4ca3-b79b-06c82b1ec72b/download00254d35bbafc33defc98866051aafd6MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8908https://repositorio.unbosque.edu.co/bitstreams/ae8f7f2a-3078-48ad-967d-e810d3214a3f/download0175ea4a2d4caec4bbcc37e300941108MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.unbosque.edu.co/bitstreams/3e8fe9bd-37a9-48d8-b2a1-1710f69d30da/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILMónica Rojas-Martínez, Leidy Yanet Serna, Mislav Jordanic_2020.pdf.jpgMónica Rojas-Martínez, Leidy Yanet Serna, Mislav Jordanic_2020.pdf.jpgIM Thumbnailimage/jpeg11530https://repositorio.unbosque.edu.co/bitstreams/25f9691e-f17c-4129-9044-7b29258fa72c/downloadb615d20978615830bbea001c5911a857MD54TEXTMónica Rojas-Martínez, Leidy Yanet Serna, Mislav Jordanic_2020.pdf.txtMónica Rojas-Martínez, Leidy Yanet Serna, Mislav Jordanic_2020.pdf.txtExtracted texttext/plain59883https://repositorio.unbosque.edu.co/bitstreams/d9647e8e-55cc-4807-8b67-bd1be4abb8c2/downloadc4df66ce75e0a992f9b5fcc938bd8c58MD5520.500.12495/5181oai:repositorio.unbosque.edu.co:20.500.12495/51812024-02-07 11:55:09.744http://creativecommons.org/licenses/by/4.0/Attribution 4.0 Internationalopen.accesshttps://repositorio.unbosque.edu.coRepositorio Institucional Universidad El Bosquebibliotecas@biteca.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 |