Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering

Autores
Gramajo, Sergio; Medina Quero, Javier; Martinez-Cruz, Carmen; Espinilla Estevez, Macarena
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The proliferation of low-cost wearable trackers is enabling users to collect daily data on human activity in a non-invasive manner and outside laboratory environments. Properly exploiting these data allows for remote supervision and counseling by experts; however, extracting key indicators from the lengthy data streams is challenging, often relying on statistical metrics or raw data clustering lacking interpretability. To address this issue, we propose an interpretable definition of key indicators using linguistic protoforms, incorporating fuzzy temporal processing and fuzzy semantic quantification. Furthermore, we utilize protoforms defined by experts to evaluate the source data stream, providing a straightforward description of users' daily activity. Subsequently, the degrees of truth of each protoform are analyzed using fuzzy clustering methods to offer an interpretable description of long-term user activity. This work includes a case study wherein data from user activity (heartbeats per minute and sleep stages) were collected using a Fitbit wearable device.
Fil: Medina Quero, Javier. University of Jan, Departament of Computer Science; Spain.
Fil: Martinez-Cruz, Carmen. University of Jan, Departament of Computer Science; Spain.
Fil: Espinilla Estevez, Macarena. University of Jan, Departament of Computer Science; Spain.
Fil: Gramajo, Sergio. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Centro de Investigación Aplicada en Tecnologías de la Información y Comunicación; Argentina.
Peer Reviewed
Materia
Fuzzy clustering
Linguistic protoforms
Wearable trackers
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2024-03-23T14:32:19Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/10020
Acceder
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network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clusteringGramajo, SergioMedina Quero, JavierMartinez-Cruz, CarmenEspinilla Estevez, MacarenaFuzzy clusteringLinguistic protoformsWearable trackersThe proliferation of low-cost wearable trackers is enabling users to collect daily data on human activity in a non-invasive manner and outside laboratory environments. Properly exploiting these data allows for remote supervision and counseling by experts; however, extracting key indicators from the lengthy data streams is challenging, often relying on statistical metrics or raw data clustering lacking interpretability. To address this issue, we propose an interpretable definition of key indicators using linguistic protoforms, incorporating fuzzy temporal processing and fuzzy semantic quantification. Furthermore, we utilize protoforms defined by experts to evaluate the source data stream, providing a straightforward description of users' daily activity. Subsequently, the degrees of truth of each protoform are analyzed using fuzzy clustering methods to offer an interpretable description of long-term user activity. This work includes a case study wherein data from user activity (heartbeats per minute and sleep stages) were collected using a Fitbit wearable device.Fil: Medina Quero, Javier. University of Jan, Departament of Computer Science; Spain.Fil: Martinez-Cruz, Carmen. University of Jan, Departament of Computer Science; Spain.Fil: Espinilla Estevez, Macarena. University of Jan, Departament of Computer Science; Spain.Fil: Gramajo, Sergio. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Centro de Investigación Aplicada en Tecnologías de la Información y Comunicación; Argentina.Peer Reviewed2024-03-23T14:32:19Z2024-03-23T14:32:19Z2020-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfJavier Medina Quero, Carmen Martinez-Cruz, Macarena Espinilla Estevez and Sergio Gramajo Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering. 24th European Conference on Artificial Intelligence-ECAI. Prestigious Applications of Intelligent Systems, PAIS 2020, Santiago de Compostella, España. 29/08-08/09 de 2020.http://hdl.handle.net/20.500.12272/10020engengCCUTIRE0005353TCinfo:eu-repo/semantics/openAccess2024-03-23T14:32:19Zhttp://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 InternacionalACL materials are Copyright © 1963–2024 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:47:36Zoai:ria.utn.edu.ar:20.500.12272/10020instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:47:37.818Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering
title Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering
spellingShingle Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering
Gramajo, Sergio
Fuzzy clustering
Linguistic protoforms
Wearable trackers
title_short Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering
title_full Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering
title_fullStr Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering
title_full_unstemmed Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering
title_sort Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering
dc.creator.none.fl_str_mv Gramajo, Sergio
Medina Quero, Javier
Martinez-Cruz, Carmen
Espinilla Estevez, Macarena
author Gramajo, Sergio
author_facet Gramajo, Sergio
Medina Quero, Javier
Martinez-Cruz, Carmen
Espinilla Estevez, Macarena
author_role author
author2 Medina Quero, Javier
Martinez-Cruz, Carmen
Espinilla Estevez, Macarena
author2_role author
author
author
dc.subject.none.fl_str_mv Fuzzy clustering
Linguistic protoforms
Wearable trackers
topic Fuzzy clustering
Linguistic protoforms
Wearable trackers
dc.description.none.fl_txt_mv The proliferation of low-cost wearable trackers is enabling users to collect daily data on human activity in a non-invasive manner and outside laboratory environments. Properly exploiting these data allows for remote supervision and counseling by experts; however, extracting key indicators from the lengthy data streams is challenging, often relying on statistical metrics or raw data clustering lacking interpretability. To address this issue, we propose an interpretable definition of key indicators using linguistic protoforms, incorporating fuzzy temporal processing and fuzzy semantic quantification. Furthermore, we utilize protoforms defined by experts to evaluate the source data stream, providing a straightforward description of users' daily activity. Subsequently, the degrees of truth of each protoform are analyzed using fuzzy clustering methods to offer an interpretable description of long-term user activity. This work includes a case study wherein data from user activity (heartbeats per minute and sleep stages) were collected using a Fitbit wearable device.
Fil: Medina Quero, Javier. University of Jan, Departament of Computer Science; Spain.
Fil: Martinez-Cruz, Carmen. University of Jan, Departament of Computer Science; Spain.
Fil: Espinilla Estevez, Macarena. University of Jan, Departament of Computer Science; Spain.
Fil: Gramajo, Sergio. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Centro de Investigación Aplicada en Tecnologías de la Información y Comunicación; Argentina.
Peer Reviewed
description The proliferation of low-cost wearable trackers is enabling users to collect daily data on human activity in a non-invasive manner and outside laboratory environments. Properly exploiting these data allows for remote supervision and counseling by experts; however, extracting key indicators from the lengthy data streams is challenging, often relying on statistical metrics or raw data clustering lacking interpretability. To address this issue, we propose an interpretable definition of key indicators using linguistic protoforms, incorporating fuzzy temporal processing and fuzzy semantic quantification. Furthermore, we utilize protoforms defined by experts to evaluate the source data stream, providing a straightforward description of users' daily activity. Subsequently, the degrees of truth of each protoform are analyzed using fuzzy clustering methods to offer an interpretable description of long-term user activity. This work includes a case study wherein data from user activity (heartbeats per minute and sleep stages) were collected using a Fitbit wearable device.
publishDate 2020
dc.date.none.fl_str_mv 2020-09-01
2024-03-23T14:32:19Z
2024-03-23T14:32:19Z
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 Javier Medina Quero, Carmen Martinez-Cruz, Macarena Espinilla Estevez and Sergio Gramajo Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering. 24th European Conference on Artificial Intelligence-ECAI. Prestigious Applications of Intelligent Systems, PAIS 2020, Santiago de Compostella, España. 29/08-08/09 de 2020.
http://hdl.handle.net/20.500.12272/10020
identifier_str_mv Javier Medina Quero, Carmen Martinez-Cruz, Macarena Espinilla Estevez and Sergio Gramajo Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering. 24th European Conference on Artificial Intelligence-ECAI. Prestigious Applications of Intelligent Systems, PAIS 2020, Santiago de Compostella, España. 29/08-08/09 de 2020.
url http://hdl.handle.net/20.500.12272/10020
dc.language.none.fl_str_mv eng
eng
language eng
dc.relation.none.fl_str_mv CCUTIRE0005353TC
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2024-03-23T14:32:19Z
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
ACL materials are Copyright © 1963–2024 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
eu_rights_str_mv openAccess
rights_invalid_str_mv 2024-03-23T14:32:19Z
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
ACL materials are Copyright © 1963–2024 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
dc.format.none.fl_str_mv pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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