sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control

The data presented in this study from the NinaPro Database are openly available at https://ninapro.hevs.ch/instructions/DB5.html (accessed on 21 March 2024). The data from UJIdb presented in this study are available upon request from the corresponding author.

Detalles Bibliográficos
Autores: Mora, Marta C., García-Ortiz, José V, Cerdá-Boluda, Joaquín
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/389660
Acceso en línea:http://hdl.handle.net/10261/389660
https://api.elsevier.com/content/abstract/scopus_id/85190280701
Access Level:acceso abierto
Palabra clave:EMG
HRI
Artificial hand
Grasping postures
Low-cost devices
Machine learning
Recognition
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spelling sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand ControlMora, Marta C.García-Ortiz, José VCerdá-Boluda, JoaquínEMGHRIArtificial handGrasping posturesLow-cost devicesMachine learningRecognitionThe data presented in this study from the NinaPro Database are openly available at https://ninapro.hevs.ch/instructions/DB5.html (accessed on 21 March 2024). The data from UJIdb presented in this study are available upon request from the corresponding author.The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human-robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots.This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness (Grant no: PID2020-118021RB-I00/AEI/10.13039/501100011033) and Universitat Jaume I (Grant no: UJI-B2022-48).Peer reviewedMultidisciplinary Digital Publishing InstituteAgencia Estatal de Investigación (España)Ministerio de Economía, Industria y Competitividad (España)Universidad Jaime IMora, Marta C. [0000-0003-0627-6764]Cerdá-Boluda, Joaquín [0000-0002-6649-298X]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/389660https://api.elsevier.com/content/abstract/scopus_id/85190280701reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118021RB-I00The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.3390/s24072063https://doi.org/10.3390/s24072063Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3896602026-05-22T06:33:51Z
dc.title.none.fl_str_mv sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control
title sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control
spellingShingle sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control
Mora, Marta C.
EMG
HRI
Artificial hand
Grasping postures
Low-cost devices
Machine learning
Recognition
title_short sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control
title_full sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control
title_fullStr sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control
title_full_unstemmed sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control
title_sort sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control
dc.creator.none.fl_str_mv Mora, Marta C.
García-Ortiz, José V
Cerdá-Boluda, Joaquín
author Mora, Marta C.
author_facet Mora, Marta C.
García-Ortiz, José V
Cerdá-Boluda, Joaquín
author_role author
author2 García-Ortiz, José V
Cerdá-Boluda, Joaquín
author2_role author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Ministerio de Economía, Industria y Competitividad (España)
Universidad Jaime I
Mora, Marta C. [0000-0003-0627-6764]
Cerdá-Boluda, Joaquín [0000-0002-6649-298X]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv EMG
HRI
Artificial hand
Grasping postures
Low-cost devices
Machine learning
Recognition
topic EMG
HRI
Artificial hand
Grasping postures
Low-cost devices
Machine learning
Recognition
description The data presented in this study from the NinaPro Database are openly available at https://ninapro.hevs.ch/instructions/DB5.html (accessed on 21 March 2024). The data from UJIdb presented in this study are available upon request from the corresponding author.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/389660
https://api.elsevier.com/content/abstract/scopus_id/85190280701
url http://hdl.handle.net/10261/389660
https://api.elsevier.com/content/abstract/scopus_id/85190280701
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118021RB-I00
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.3390/s24072063
https://doi.org/10.3390/s24072063
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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