Multidirectional bending sensor using capillary fibers and machine learning for real-time applications

In this article, the design and implementation of a bidirectional curvature sensor based on a fiber-optic interferometer are presented. The sensor structure was fabricated by fusing a capillary fiber fragment between single-mode fibers (SMFs), with the addition of a long end capillary to promote a l...

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Autores: Vanegas Tenezaca, Evelyn Dayanara, Galarza Galarza, Marko, Dauliat, Romain, Jamier, Raphael, Roy, Philippe, López-Amo Sáinz, Manuel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/54309
Acceso en línea:https://hdl.handle.net/2454/54309
Access Level:acceso abierto
Palabra clave:Bend
Capillary fiber
Curvature
Machine learning
Optical fiber sensor
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spelling Multidirectional bending sensor using capillary fibers and machine learning for real-time applicationsVanegas Tenezaca, Evelyn DayanaraGalarza Galarza, MarkoDauliat, RomainJamier, RaphaelRoy, PhilippeLópez-Amo Sáinz, ManuelBendCapillary fiberCurvatureMachine learningOptical fiber sensorIn this article, the design and implementation of a bidirectional curvature sensor based on a fiber-optic interferometer are presented. The sensor structure was fabricated by fusing a capillary fiber fragment between single-mode fibers (SMFs), with the addition of a long end capillary to promote a long interferometric section, forming a Fabry-Perot (FP) cavity. Detailed analysis of the curvature data was carried out using machine learning techniques, allowing accurate classification of curvature in both directions of rotation. The experimental results showed excellent agreement (R2: 0.9998) with the predicted values. The sensor exhibits a maximum error of 1.9485°. This approach presents significant potential for applications requiring accurate real-time curvature measurements.This work was supported in part by CIN/AEI/10.13039/501100011033 and FEDER "AWay to Make Europe" under Project PID2022-137269OB and in part by MCIN/AEI/10.13039/501100011033 and European Union "Next Generation EU"/PRTR under Project TED2021-130378B. Open access funding provided by Universidad Publica de Navarra.IEEEIngeniería Eléctrica, Electrónica y de ComunicaciónIngeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio IngeniaritzaInstitute of Smart Cities - ISCUniversidad Publica de Navarra / Nafarroako Unibertsitate Publikoa2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/54309reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137269OB-C21info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-130378B-C22© 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/543092026-06-17T12:41:47Z
dc.title.none.fl_str_mv Multidirectional bending sensor using capillary fibers and machine learning for real-time applications
title Multidirectional bending sensor using capillary fibers and machine learning for real-time applications
spellingShingle Multidirectional bending sensor using capillary fibers and machine learning for real-time applications
Vanegas Tenezaca, Evelyn Dayanara
Bend
Capillary fiber
Curvature
Machine learning
Optical fiber sensor
title_short Multidirectional bending sensor using capillary fibers and machine learning for real-time applications
title_full Multidirectional bending sensor using capillary fibers and machine learning for real-time applications
title_fullStr Multidirectional bending sensor using capillary fibers and machine learning for real-time applications
title_full_unstemmed Multidirectional bending sensor using capillary fibers and machine learning for real-time applications
title_sort Multidirectional bending sensor using capillary fibers and machine learning for real-time applications
dc.creator.none.fl_str_mv Vanegas Tenezaca, Evelyn Dayanara
Galarza Galarza, Marko
Dauliat, Romain
Jamier, Raphael
Roy, Philippe
López-Amo Sáinz, Manuel
author Vanegas Tenezaca, Evelyn Dayanara
author_facet Vanegas Tenezaca, Evelyn Dayanara
Galarza Galarza, Marko
Dauliat, Romain
Jamier, Raphael
Roy, Philippe
López-Amo Sáinz, Manuel
author_role author
author2 Galarza Galarza, Marko
Dauliat, Romain
Jamier, Raphael
Roy, Philippe
López-Amo Sáinz, Manuel
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería Eléctrica, Electrónica y de Comunicación
Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza
Institute of Smart Cities - ISC
Universidad Publica de Navarra / Nafarroako Unibertsitate Publikoa
dc.subject.none.fl_str_mv Bend
Capillary fiber
Curvature
Machine learning
Optical fiber sensor
topic Bend
Capillary fiber
Curvature
Machine learning
Optical fiber sensor
description In this article, the design and implementation of a bidirectional curvature sensor based on a fiber-optic interferometer are presented. The sensor structure was fabricated by fusing a capillary fiber fragment between single-mode fibers (SMFs), with the addition of a long end capillary to promote a long interferometric section, forming a Fabry-Perot (FP) cavity. Detailed analysis of the curvature data was carried out using machine learning techniques, allowing accurate classification of curvature in both directions of rotation. The experimental results showed excellent agreement (R2: 0.9998) with the predicted values. The sensor exhibits a maximum error of 1.9485°. This approach presents significant potential for applications requiring accurate real-time curvature measurements.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/54309
url https://hdl.handle.net/2454/54309
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137269OB-C21
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-130378B-C22
dc.rights.none.fl_str_mv © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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