Detection of activities in bathrooms through deep learning and environmental data graphics images

Automatic detection activities in indoor spaces has been and is a matter of great interest. Thus, in the field of health surveillance, one of the spaces frequently studied is the bathroom of homes and specifically the behaviour of users in the said space, since certain pathologies can sometimes be d...

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Autores: Marín García, David, Bienvenido Huertas, José David, Moyano, Juan, Rubio Bellido, Carlos, Rodríguez Jiménez, Carlos Eugenio
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/156609
Acceso en línea:https://hdl.handle.net/11441/156609
https://doi.org/10.1016/j.heliyon.2024.e26942
Access Level:acceso abierto
Palabra clave:Activity recognition
Bathrooms
Environment
CNN image classification
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spelling Detection of activities in bathrooms through deep learning and environmental data graphics imagesMarín García, DavidBienvenido Huertas, José DavidMoyano, JuanRubio Bellido, CarlosRodríguez Jiménez, Carlos EugenioActivity recognitionBathroomsEnvironmentCNN image classificationAutomatic detection activities in indoor spaces has been and is a matter of great interest. Thus, in the field of health surveillance, one of the spaces frequently studied is the bathroom of homes and specifically the behaviour of users in the said space, since certain pathologies can sometimes be deduced from it. That is why, the objective of this study is to know if it is possible to automatically classify the main activities that occur within the bathroom, using an innovative methodology with respect to the methods used to date, based on environmental parameters and the application of machine learning algorithms, thus allowing privacy to be preserved, which is a notable improvement in relation to other methods. For this, the methodology followed is based on the novel application of a pre-trained convolutional network for classifying graphs resulting from the monitoring of the environmental parameters of a bathroom. The results obtained allow us to conclude that, in addition to being able to check whether environmental data are adequate for health, it is possible to detect a high rate of true positives (around 80%) in some of the most frequent and important activities, thus facilitating its automation in a very simple and economical way.ElsevierExpresión Gráfica e Ingeniería en la EdificaciónConstrucciones Arquitectónicas IITEP970: Innovación Tecnológica, Sistemas de Modelado 3d y Diagnosis Energética en Patrimonio y EdificaciónRNM162: Composición, Arquitectura y Medio Ambiente2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/156609https://doi.org/10.1016/j.heliyon.2024.e26942reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésHeliyon, 10(6) (e26942).https://www.sciencedirect.com/science/article/pii/S2405844024029736?via%3Dihubinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1566092026-06-17T12:51:07Z
dc.title.none.fl_str_mv Detection of activities in bathrooms through deep learning and environmental data graphics images
title Detection of activities in bathrooms through deep learning and environmental data graphics images
spellingShingle Detection of activities in bathrooms through deep learning and environmental data graphics images
Marín García, David
Activity recognition
Bathrooms
Environment
CNN image classification
title_short Detection of activities in bathrooms through deep learning and environmental data graphics images
title_full Detection of activities in bathrooms through deep learning and environmental data graphics images
title_fullStr Detection of activities in bathrooms through deep learning and environmental data graphics images
title_full_unstemmed Detection of activities in bathrooms through deep learning and environmental data graphics images
title_sort Detection of activities in bathrooms through deep learning and environmental data graphics images
dc.creator.none.fl_str_mv Marín García, David
Bienvenido Huertas, José David
Moyano, Juan
Rubio Bellido, Carlos
Rodríguez Jiménez, Carlos Eugenio
author Marín García, David
author_facet Marín García, David
Bienvenido Huertas, José David
Moyano, Juan
Rubio Bellido, Carlos
Rodríguez Jiménez, Carlos Eugenio
author_role author
author2 Bienvenido Huertas, José David
Moyano, Juan
Rubio Bellido, Carlos
Rodríguez Jiménez, Carlos Eugenio
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Expresión Gráfica e Ingeniería en la Edificación
Construcciones Arquitectónicas II
TEP970: Innovación Tecnológica, Sistemas de Modelado 3d y Diagnosis Energética en Patrimonio y Edificación
RNM162: Composición, Arquitectura y Medio Ambiente
dc.subject.none.fl_str_mv Activity recognition
Bathrooms
Environment
CNN image classification
topic Activity recognition
Bathrooms
Environment
CNN image classification
description Automatic detection activities in indoor spaces has been and is a matter of great interest. Thus, in the field of health surveillance, one of the spaces frequently studied is the bathroom of homes and specifically the behaviour of users in the said space, since certain pathologies can sometimes be deduced from it. That is why, the objective of this study is to know if it is possible to automatically classify the main activities that occur within the bathroom, using an innovative methodology with respect to the methods used to date, based on environmental parameters and the application of machine learning algorithms, thus allowing privacy to be preserved, which is a notable improvement in relation to other methods. For this, the methodology followed is based on the novel application of a pre-trained convolutional network for classifying graphs resulting from the monitoring of the environmental parameters of a bathroom. The results obtained allow us to conclude that, in addition to being able to check whether environmental data are adequate for health, it is possible to detect a high rate of true positives (around 80%) in some of the most frequent and important activities, thus facilitating its automation in a very simple and economical way.
publishDate 2024
dc.date.none.fl_str_mv 2024
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/11441/156609
https://doi.org/10.1016/j.heliyon.2024.e26942
url https://hdl.handle.net/11441/156609
https://doi.org/10.1016/j.heliyon.2024.e26942
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Heliyon, 10(6) (e26942).
https://www.sciencedirect.com/science/article/pii/S2405844024029736?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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