Criterion validity of neural networks to assess lower limb motion during cycling

The use of marker-less methods to automatically obtain kinematics of movement is expanding but validity to high-velocity tasks such as cycling with the presence of the bicycle on the field of view is needed when standard video footage is obtained. The purpose of this study was to assess if pre-train...

Descripción completa

Detalles Bibliográficos
Autores: Bini, Rodrigo, Serrancolí, Gil|||0000-0001-5034-2445, Santiago, Paulo, Pinto, Allan, Moura, Felipe
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/386964
Acceso en línea:https://hdl.handle.net/2117/386964
https://dx.doi.org/10.1080/02640414.2023.2194725
Access Level:acceso abierto
Palabra clave:Biomechanics
Kinematics
Movement analysis
Bicycle
Machine learning
Joint kinematics
Biomecànica
Cinemàtica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomecànica
id ES_30d6bd3e916f87ec4cdd36e4a8255e8a
oai_identifier_str oai:upcommons.upc.edu:2117/386964
network_acronym_str ES
network_name_str España
repository_id_str
spelling Criterion validity of neural networks to assess lower limb motion during cyclingBini, RodrigoSerrancolí, Gil|||0000-0001-5034-2445Santiago, PauloPinto, AllanMoura, FelipeBiomechanicsKinematicsMovement analysisBicycleMachine learningJoint kinematicsBiomecànicaCinemàticaÀrees temàtiques de la UPC::Enginyeria biomèdica::BiomecànicaThe use of marker-less methods to automatically obtain kinematics of movement is expanding but validity to high-velocity tasks such as cycling with the presence of the bicycle on the field of view is needed when standard video footage is obtained. The purpose of this study was to assess if pre-trained neural networks are valid for calculations of lower limb joint kinematics during cycling. Motion of twenty-six cyclists pedalling on a cycle trainer was captured by a video camera capturing frames from the sagittal plane whilst reflective markers were attached to their lower limb. The marker-tracking method was compared to two established deep learning-based approaches (Microsoft Research Asia-MSRA and OpenPose) to estimate hip, knee and ankle joint angles. Poor to moderate agreement was found for both methods, with OpenPose differing from the criterion by 4–8° for the hip and knee joints. Larger errors were observed for the ankle joint (15–22°) but no significant differences between methods throughout the crank cycle when assessed using Statistical Parametric Mapping were observed for any of the joints. OpenPose presented stronger agreement with marker-tracking (criterion) than the MSRA for the hip and knee joints but resulted in poor agreement for the ankle joint.Peer Reviewed20232023-03-2820232023-05-03journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://hdl.handle.net/2117/386964https://dx.doi.org/10.1080/02640414.2023.2194725reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3869642026-05-27T15:37:01Z
dc.title.none.fl_str_mv Criterion validity of neural networks to assess lower limb motion during cycling
title Criterion validity of neural networks to assess lower limb motion during cycling
spellingShingle Criterion validity of neural networks to assess lower limb motion during cycling
Bini, Rodrigo
Biomechanics
Kinematics
Movement analysis
Bicycle
Machine learning
Joint kinematics
Biomecànica
Cinemàtica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomecànica
title_short Criterion validity of neural networks to assess lower limb motion during cycling
title_full Criterion validity of neural networks to assess lower limb motion during cycling
title_fullStr Criterion validity of neural networks to assess lower limb motion during cycling
title_full_unstemmed Criterion validity of neural networks to assess lower limb motion during cycling
title_sort Criterion validity of neural networks to assess lower limb motion during cycling
dc.creator.none.fl_str_mv Bini, Rodrigo
Serrancolí, Gil|||0000-0001-5034-2445
Santiago, Paulo
Pinto, Allan
Moura, Felipe
author Bini, Rodrigo
author_facet Bini, Rodrigo
Serrancolí, Gil|||0000-0001-5034-2445
Santiago, Paulo
Pinto, Allan
Moura, Felipe
author_role author
author2 Serrancolí, Gil|||0000-0001-5034-2445
Santiago, Paulo
Pinto, Allan
Moura, Felipe
author2_role author
author
author
author
dc.subject.none.fl_str_mv Biomechanics
Kinematics
Movement analysis
Bicycle
Machine learning
Joint kinematics
Biomecànica
Cinemàtica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomecànica
topic Biomechanics
Kinematics
Movement analysis
Bicycle
Machine learning
Joint kinematics
Biomecànica
Cinemàtica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomecànica
description The use of marker-less methods to automatically obtain kinematics of movement is expanding but validity to high-velocity tasks such as cycling with the presence of the bicycle on the field of view is needed when standard video footage is obtained. The purpose of this study was to assess if pre-trained neural networks are valid for calculations of lower limb joint kinematics during cycling. Motion of twenty-six cyclists pedalling on a cycle trainer was captured by a video camera capturing frames from the sagittal plane whilst reflective markers were attached to their lower limb. The marker-tracking method was compared to two established deep learning-based approaches (Microsoft Research Asia-MSRA and OpenPose) to estimate hip, knee and ankle joint angles. Poor to moderate agreement was found for both methods, with OpenPose differing from the criterion by 4–8° for the hip and knee joints. Larger errors were observed for the ankle joint (15–22°) but no significant differences between methods throughout the crank cycle when assessed using Statistical Parametric Mapping were observed for any of the joints. OpenPose presented stronger agreement with marker-tracking (criterion) than the MSRA for the hip and knee joints but resulted in poor agreement for the ankle joint.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-03-28
2023
2023-05-03
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/386964
https://dx.doi.org/10.1080/02640414.2023.2194725
url https://hdl.handle.net/2117/386964
https://dx.doi.org/10.1080/02640414.2023.2194725
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869405571559456768
score 15,300719

Similares en LA Referencia