Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds

Autores
Abraham, Leandro; Bromberg, Facundo; Forradellas, Raymundo Quilez
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background: Muscle activation level is currently being captured using impractical and expensive devices which make their use in telemedicine settings extremely difficult. To address this issue, a prototype is presented of a non-invasive, easy-to-install system for the estimation of a discrete level of muscle activation of the biceps muscle from 3D point clouds captured with RGB-D cameras. Methods: A methodology is proposed that uses the ensemble of shape functions point cloud descriptor for the geometric characterization of 3D point clouds, together with support vector machines to learn a classifier that, based on this geometric characterization for some points of view of the biceps, provides a model for the estimation of muscle activation for all neighboring points of view. This results in a classifier that is robust to small perturbations in the point of view of the capturing device, greatly simplifying the installation process for end-users. Results: In the discrimination of five levels of effort with values up to the maximum voluntary contraction (MVC) of the biceps muscle (3800 g), the best variant of the proposed methodology achieved mean absolute errors of about 9.21% MVC — an acceptable performance for telemedicine settings where the electric measurement of muscle activation is impractical. Conclusions: The results prove that the correlations between the external geometry of the arm and biceps muscle activation are strong enough to consider computer vision and supervised learning an alternative with great potential for practical applications in tele-physiotherapy.
Fil: Abraham, Leandro. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio DHARMA; Argentina. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Bromberg, Facundo. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio DHARMA; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Forradellas, Raymundo Quilez. Universidad Nacional de Cuyo; Argentina
Materia
3D POINT CLOUDS
BICEPS ACTIVATION ESTIMATION
BIOMECHANICS
ENSEMBLE OF SHAPE FUNCTIONS
SUPPORT VECTOR MACHINES
TELE-PHYSIOTHERAPY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/88221
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/88221
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point cloudsAbraham, LeandroBromberg, FacundoForradellas, Raymundo Quilez3D POINT CLOUDSBICEPS ACTIVATION ESTIMATIONBIOMECHANICSENSEMBLE OF SHAPE FUNCTIONSSUPPORT VECTOR MACHINESTELE-PHYSIOTHERAPYhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Background: Muscle activation level is currently being captured using impractical and expensive devices which make their use in telemedicine settings extremely difficult. To address this issue, a prototype is presented of a non-invasive, easy-to-install system for the estimation of a discrete level of muscle activation of the biceps muscle from 3D point clouds captured with RGB-D cameras. Methods: A methodology is proposed that uses the ensemble of shape functions point cloud descriptor for the geometric characterization of 3D point clouds, together with support vector machines to learn a classifier that, based on this geometric characterization for some points of view of the biceps, provides a model for the estimation of muscle activation for all neighboring points of view. This results in a classifier that is robust to small perturbations in the point of view of the capturing device, greatly simplifying the installation process for end-users. Results: In the discrimination of five levels of effort with values up to the maximum voluntary contraction (MVC) of the biceps muscle (3800 g), the best variant of the proposed methodology achieved mean absolute errors of about 9.21% MVC — an acceptable performance for telemedicine settings where the electric measurement of muscle activation is impractical. Conclusions: The results prove that the correlations between the external geometry of the arm and biceps muscle activation are strong enough to consider computer vision and supervised learning an alternative with great potential for practical applications in tele-physiotherapy.Fil: Abraham, Leandro. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio DHARMA; Argentina. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Bromberg, Facundo. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio DHARMA; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Forradellas, Raymundo Quilez. Universidad Nacional de Cuyo; ArgentinaPergamon-Elsevier Science Ltd2018-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/88221Abraham, Leandro; Bromberg, Facundo; Forradellas, Raymundo Quilez; Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds; Pergamon-Elsevier Science Ltd; Computers In Biology And Medicine; 95; 4-2018; 129-1390010-4825CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compbiomed.2018.02.011info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0010482518300416info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:47:50Zoai:ri.conicet.gov.ar:11336/88221instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:47:50.84CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds
title Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds
spellingShingle Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds
Abraham, Leandro
3D POINT CLOUDS
BICEPS ACTIVATION ESTIMATION
BIOMECHANICS
ENSEMBLE OF SHAPE FUNCTIONS
SUPPORT VECTOR MACHINES
TELE-PHYSIOTHERAPY
title_short Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds
title_full Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds
title_fullStr Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds
title_full_unstemmed Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds
title_sort Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds
dc.creator.none.fl_str_mv Abraham, Leandro
Bromberg, Facundo
Forradellas, Raymundo Quilez
author Abraham, Leandro
author_facet Abraham, Leandro
Bromberg, Facundo
Forradellas, Raymundo Quilez
author_role author
author2 Bromberg, Facundo
Forradellas, Raymundo Quilez
author2_role author
author
dc.subject.none.fl_str_mv 3D POINT CLOUDS
BICEPS ACTIVATION ESTIMATION
BIOMECHANICS
ENSEMBLE OF SHAPE FUNCTIONS
SUPPORT VECTOR MACHINES
TELE-PHYSIOTHERAPY
topic 3D POINT CLOUDS
BICEPS ACTIVATION ESTIMATION
BIOMECHANICS
ENSEMBLE OF SHAPE FUNCTIONS
SUPPORT VECTOR MACHINES
TELE-PHYSIOTHERAPY
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Background: Muscle activation level is currently being captured using impractical and expensive devices which make their use in telemedicine settings extremely difficult. To address this issue, a prototype is presented of a non-invasive, easy-to-install system for the estimation of a discrete level of muscle activation of the biceps muscle from 3D point clouds captured with RGB-D cameras. Methods: A methodology is proposed that uses the ensemble of shape functions point cloud descriptor for the geometric characterization of 3D point clouds, together with support vector machines to learn a classifier that, based on this geometric characterization for some points of view of the biceps, provides a model for the estimation of muscle activation for all neighboring points of view. This results in a classifier that is robust to small perturbations in the point of view of the capturing device, greatly simplifying the installation process for end-users. Results: In the discrimination of five levels of effort with values up to the maximum voluntary contraction (MVC) of the biceps muscle (3800 g), the best variant of the proposed methodology achieved mean absolute errors of about 9.21% MVC — an acceptable performance for telemedicine settings where the electric measurement of muscle activation is impractical. Conclusions: The results prove that the correlations between the external geometry of the arm and biceps muscle activation are strong enough to consider computer vision and supervised learning an alternative with great potential for practical applications in tele-physiotherapy.
Fil: Abraham, Leandro. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio DHARMA; Argentina. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Bromberg, Facundo. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio DHARMA; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Forradellas, Raymundo Quilez. Universidad Nacional de Cuyo; Argentina
description Background: Muscle activation level is currently being captured using impractical and expensive devices which make their use in telemedicine settings extremely difficult. To address this issue, a prototype is presented of a non-invasive, easy-to-install system for the estimation of a discrete level of muscle activation of the biceps muscle from 3D point clouds captured with RGB-D cameras. Methods: A methodology is proposed that uses the ensemble of shape functions point cloud descriptor for the geometric characterization of 3D point clouds, together with support vector machines to learn a classifier that, based on this geometric characterization for some points of view of the biceps, provides a model for the estimation of muscle activation for all neighboring points of view. This results in a classifier that is robust to small perturbations in the point of view of the capturing device, greatly simplifying the installation process for end-users. Results: In the discrimination of five levels of effort with values up to the maximum voluntary contraction (MVC) of the biceps muscle (3800 g), the best variant of the proposed methodology achieved mean absolute errors of about 9.21% MVC — an acceptable performance for telemedicine settings where the electric measurement of muscle activation is impractical. Conclusions: The results prove that the correlations between the external geometry of the arm and biceps muscle activation are strong enough to consider computer vision and supervised learning an alternative with great potential for practical applications in tele-physiotherapy.
publishDate 2018
dc.date.none.fl_str_mv 2018-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/88221
Abraham, Leandro; Bromberg, Facundo; Forradellas, Raymundo Quilez; Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds; Pergamon-Elsevier Science Ltd; Computers In Biology And Medicine; 95; 4-2018; 129-139
0010-4825
CONICET Digital
CONICET
url http://hdl.handle.net/11336/88221
identifier_str_mv Abraham, Leandro; Bromberg, Facundo; Forradellas, Raymundo Quilez; Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds; Pergamon-Elsevier Science Ltd; Computers In Biology And Medicine; 95; 4-2018; 129-139
0010-4825
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compbiomed.2018.02.011
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0010482518300416
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613489740480512
score 13.070432

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