Functional data analysis of ground reaction forces combined with clinical measures for early knee osteoarthritis detection

[EN] Early detection of knee osteoarthritis (KOA) is essential to improve treatment outcomes and reduce its long-term impact. However, early diagnosis of KOA (EKOA) remains difficult due to the absence of standardised criteria and the subtle or intermittent nature of early symptoms. This study evalu...

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Autores: Aragon-Basanta, Elisa, Ayala, Guillermo, Viosca-Herrero, Enrique, Alabajos-Cea, Ana, Herrero-Manley, Luz, Page Del Pozo, Alvaro Felipe|||0000-0002-5432-310X
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/231232
Acceso en línea:https://riunet.upv.es/handle/10251/231232
Access Level:acceso abierto
Palabra clave:Early knee osteoarthritis (EKOA)
Functional Data Analysis (FDA)
Functional logistic regression
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network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Functional data analysis of ground reaction forces combined with clinical measures for early knee osteoarthritis detection
title Functional data analysis of ground reaction forces combined with clinical measures for early knee osteoarthritis detection
spellingShingle Functional data analysis of ground reaction forces combined with clinical measures for early knee osteoarthritis detection
Aragon-Basanta, Elisa
Early knee osteoarthritis (EKOA)
Functional Data Analysis (FDA)
Functional logistic regression
title_short Functional data analysis of ground reaction forces combined with clinical measures for early knee osteoarthritis detection
title_full Functional data analysis of ground reaction forces combined with clinical measures for early knee osteoarthritis detection
title_fullStr Functional data analysis of ground reaction forces combined with clinical measures for early knee osteoarthritis detection
title_full_unstemmed Functional data analysis of ground reaction forces combined with clinical measures for early knee osteoarthritis detection
title_sort Functional data analysis of ground reaction forces combined with clinical measures for early knee osteoarthritis detection
dc.creator.none.fl_str_mv Aragon-Basanta, Elisa
Ayala, Guillermo
Viosca-Herrero, Enrique
Alabajos-Cea, Ana
Herrero-Manley, Luz
Page Del Pozo, Alvaro Felipe|||0000-0002-5432-310X
author Aragon-Basanta, Elisa
author_facet Aragon-Basanta, Elisa
Ayala, Guillermo
Viosca-Herrero, Enrique
Alabajos-Cea, Ana
Herrero-Manley, Luz
Page Del Pozo, Alvaro Felipe|||0000-0002-5432-310X
author_role author
author2 Ayala, Guillermo
Viosca-Herrero, Enrique
Alabajos-Cea, Ana
Herrero-Manley, Luz
Page Del Pozo, Alvaro Felipe|||0000-0002-5432-310X
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Física Aplicada
Escuela Técnica Superior de Ingeniería de Telecomunicación
Instituto Universitario de Investigación Concertado de Ingeniería Mecánica y Biomecánica
European Commission
Generalitat Valenciana
Agencia Estatal de Investigación
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Early knee osteoarthritis (EKOA)
Functional Data Analysis (FDA)
Functional logistic regression
topic Early knee osteoarthritis (EKOA)
Functional Data Analysis (FDA)
Functional logistic regression
description [EN] Early detection of knee osteoarthritis (KOA) is essential to improve treatment outcomes and reduce its long-term impact. However, early diagnosis of KOA (EKOA) remains difficult due to the absence of standardised criteria and the subtle or intermittent nature of early symptoms. This study evaluates the utility of Functional Logistic Regression (FLR) as a robust and efficient classifier that integrates clinical data and biomechanical signals-specifically, ground reaction force (GRF) curves represented through Functional Data Analysis-to support EKOA diagnosis. Fifty-six individuals with knee pain were assessed; 25 were diagnosed with EKOA based on Mahmoudian's clinical criteria, and 31 were classified as healthy. Anthropometric data and pain scores (Visual Analogue Scale, VAS) were collected, and GRF signals in three directions (vertical, anteroposterior, and mediolateral) were recorded during walking trials. A FLR model was developed by combining GRF curves, anthropometric measurements, and VAS scores. The mediolateral GRF component showed the strongest discriminative power. The model that combined biomechanical and clinical variables outperformed models using either data source alone, achieving higher accuracy and sensitivity while maintaining parsimony and robustness. Functional Logistic Regression offers key advantages over classical logistic models based on scalar features and black-box machine learning approaches: it allows the direct use of biomechanical signals without prior discretisation and reduces the risk of overfitting while preserving statistical rigour. These results support FLR as a robust and efficient method for early KOA classification based on combined clinical and biomechanical data.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-11-18
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/231232
url https://riunet.upv.es/handle/10251/231232
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-117114GB-I00 APLICACION DE MODELOS ESTADISTICOS REGULARIZADOS A PROBLEMAS EN BIOINFORMATICA, RECUPERACION DE IMAGENES BASADA EN CONTENIDO Y CLASIFICACION DE IMAGENES Y SEÑALES BIOMEDICAS
European Commission https://doi.org/10.13039/501100000780 H2020 777159 Advanced personalised, multi-scale computer models preventing OsteoArthritis
Generalitat Valenciana https://doi.org/10.13039/501100003359 CIPROM%2F2023%2F066
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
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
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
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spelling Functional data analysis of ground reaction forces combined with clinical measures for early knee osteoarthritis detectionAragon-Basanta, ElisaAyala, GuillermoViosca-Herrero, EnriqueAlabajos-Cea, AnaHerrero-Manley, LuzPage Del Pozo, Alvaro Felipe|||0000-0002-5432-310XEarly knee osteoarthritis (EKOA)Functional Data Analysis (FDA)Functional logistic regression[EN] Early detection of knee osteoarthritis (KOA) is essential to improve treatment outcomes and reduce its long-term impact. However, early diagnosis of KOA (EKOA) remains difficult due to the absence of standardised criteria and the subtle or intermittent nature of early symptoms. This study evaluates the utility of Functional Logistic Regression (FLR) as a robust and efficient classifier that integrates clinical data and biomechanical signals-specifically, ground reaction force (GRF) curves represented through Functional Data Analysis-to support EKOA diagnosis. Fifty-six individuals with knee pain were assessed; 25 were diagnosed with EKOA based on Mahmoudian's clinical criteria, and 31 were classified as healthy. Anthropometric data and pain scores (Visual Analogue Scale, VAS) were collected, and GRF signals in three directions (vertical, anteroposterior, and mediolateral) were recorded during walking trials. A FLR model was developed by combining GRF curves, anthropometric measurements, and VAS scores. The mediolateral GRF component showed the strongest discriminative power. The model that combined biomechanical and clinical variables outperformed models using either data source alone, achieving higher accuracy and sensitivity while maintaining parsimony and robustness. Functional Logistic Regression offers key advantages over classical logistic models based on scalar features and black-box machine learning approaches: it allows the direct use of biomechanical signals without prior discretisation and reduces the risk of overfitting while preserving statistical rigour. These results support FLR as a robust and efficient method for early KOA classification based on combined clinical and biomechanical data.This publication is part of the I+D+i project PGC type B with reference PID2020-117114GB-I00 funded by the Spanish Ministry of Science, Innovation and Universities, MCIN/AEI/10.13039/501100011033/ (G. Ayala) and project CIPROM/2023/066 (G. Ayala). This project received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 777159. Linea de investigacion artrosis de rodilla. Referencia #00700409. La Fe Health Research Institute. Avinguda de Fernando Abril Martorell, 106, 46026, Valencia. Spain.Nature Publishing GroupDepartamento de Física AplicadaEscuela Técnica Superior de Ingeniería de TelecomunicaciónInstituto Universitario de Investigación Concertado de Ingeniería Mecánica y BiomecánicaEuropean CommissionGeneralitat ValencianaAgencia Estatal de InvestigaciónRepositorio Institucional de la Universitat Politècnica de València Riunet20252025-11-18journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/231232reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-117114GB-I00 APLICACION DE MODELOS ESTADISTICOS REGULARIZADOS A PROBLEMAS EN BIOINFORMATICA, RECUPERACION DE IMAGENES BASADA EN CONTENIDO Y CLASIFICACION DE IMAGENES Y SEÑALES BIOMEDICASEuropean Commission https://doi.org/10.13039/501100000780 H2020 777159 Advanced personalised, multi-scale computer models preventing OsteoArthritisGeneralitat Valenciana https://doi.org/10.13039/501100003359 CIPROM%2F2023%2F066open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2312322026-06-13T07:49:27Z
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