Wearable physiological signals under acute stress and exercise conditions

Autores
Hongn, Andrea; Bosch, Facundo; Prado, Lara Eleonora; Ferrández, José Manuel; Bonomini, Maria Paula
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work, a novel dataset containing physiological signals recorded non invasevely during structured acute stress induction, as well as aerobic and anaerobic exercise sessions is presented. The physiological data were collected using the Empatica E4, a wearable device that measures electrodermal activity, skin temperature, three-axis accelerometry and blood volume pulse, from which heart rate and heart rate variability features can be derived. A stress induction protocol was designed using mathematical and emotional tasks to elicit physiological responses. For aerobic and anaerobic exercise, a stationary bike routine was developed to distinguish between the two types of activity. The dataset includes records from 36 healthy individuals during the stress protocol, 30 during aerobic exercise, and 31 during anaerobic exercise. Several machine learning algorithms were applied to validate the dataset, with XGBoost achieving an accuracy of 93% in classifying stress versus rest, 91% in distinguishing between aerobic and anaerobic exercise, and 84% in a four-label classification task involving stress, rest, aerobic, and anaerobic activities. The dataset is publicly available for further research.
Fil: Hongn, Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina
Fil: Bosch, Facundo. Instituto Tecnológico de Buenos Aires; Argentina
Fil: Prado, Lara Eleonora. Instituto Tecnológico de Buenos Aires; Argentina
Fil: Ferrández, José Manuel. Universidad Politécnica de Cartagena; España
Fil: Bonomini, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina. Instituto Tecnológico de Buenos Aires; Argentina
Materia
STRESS
WEARABLE
EXERCISE
ANAEROBIC
AEROBIC
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/263256
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Wearable physiological signals under acute stress and exercise conditionsHongn, AndreaBosch, FacundoPrado, Lara EleonoraFerrández, José ManuelBonomini, Maria PaulaSTRESSWEARABLEEXERCISEANAEROBICAEROBIChttps://purl.org/becyt/ford/2.6https://purl.org/becyt/ford/2In this work, a novel dataset containing physiological signals recorded non invasevely during structured acute stress induction, as well as aerobic and anaerobic exercise sessions is presented. The physiological data were collected using the Empatica E4, a wearable device that measures electrodermal activity, skin temperature, three-axis accelerometry and blood volume pulse, from which heart rate and heart rate variability features can be derived. A stress induction protocol was designed using mathematical and emotional tasks to elicit physiological responses. For aerobic and anaerobic exercise, a stationary bike routine was developed to distinguish between the two types of activity. The dataset includes records from 36 healthy individuals during the stress protocol, 30 during aerobic exercise, and 31 during anaerobic exercise. Several machine learning algorithms were applied to validate the dataset, with XGBoost achieving an accuracy of 93% in classifying stress versus rest, 91% in distinguishing between aerobic and anaerobic exercise, and 84% in a four-label classification task involving stress, rest, aerobic, and anaerobic activities. The dataset is publicly available for further research.Fil: Hongn, Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; ArgentinaFil: Bosch, Facundo. Instituto Tecnológico de Buenos Aires; ArgentinaFil: Prado, Lara Eleonora. Instituto Tecnológico de Buenos Aires; ArgentinaFil: Ferrández, José Manuel. Universidad Politécnica de Cartagena; EspañaFil: Bonomini, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina. Instituto Tecnológico de Buenos Aires; ArgentinaSpringer2025-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/263256Hongn, Andrea; Bosch, Facundo; Prado, Lara Eleonora; Ferrández, José Manuel; Bonomini, Maria Paula; Wearable physiological signals under acute stress and exercise conditions; Springer; Scientific Data; 12; 1; 3-2025; 1-102052-4463CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41597-025-04845-9info:eu-repo/semantics/altIdentifier/doi/10.1038/s41597-025-04845-9info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:02:07Zoai:ri.conicet.gov.ar:11336/263256instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:02:07.77CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Wearable physiological signals under acute stress and exercise conditions
title Wearable physiological signals under acute stress and exercise conditions
spellingShingle Wearable physiological signals under acute stress and exercise conditions
Hongn, Andrea
STRESS
WEARABLE
EXERCISE
ANAEROBIC
AEROBIC
title_short Wearable physiological signals under acute stress and exercise conditions
title_full Wearable physiological signals under acute stress and exercise conditions
title_fullStr Wearable physiological signals under acute stress and exercise conditions
title_full_unstemmed Wearable physiological signals under acute stress and exercise conditions
title_sort Wearable physiological signals under acute stress and exercise conditions
dc.creator.none.fl_str_mv Hongn, Andrea
Bosch, Facundo
Prado, Lara Eleonora
Ferrández, José Manuel
Bonomini, Maria Paula
author Hongn, Andrea
author_facet Hongn, Andrea
Bosch, Facundo
Prado, Lara Eleonora
Ferrández, José Manuel
Bonomini, Maria Paula
author_role author
author2 Bosch, Facundo
Prado, Lara Eleonora
Ferrández, José Manuel
Bonomini, Maria Paula
author2_role author
author
author
author
dc.subject.none.fl_str_mv STRESS
WEARABLE
EXERCISE
ANAEROBIC
AEROBIC
topic STRESS
WEARABLE
EXERCISE
ANAEROBIC
AEROBIC
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.6
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work, a novel dataset containing physiological signals recorded non invasevely during structured acute stress induction, as well as aerobic and anaerobic exercise sessions is presented. The physiological data were collected using the Empatica E4, a wearable device that measures electrodermal activity, skin temperature, three-axis accelerometry and blood volume pulse, from which heart rate and heart rate variability features can be derived. A stress induction protocol was designed using mathematical and emotional tasks to elicit physiological responses. For aerobic and anaerobic exercise, a stationary bike routine was developed to distinguish between the two types of activity. The dataset includes records from 36 healthy individuals during the stress protocol, 30 during aerobic exercise, and 31 during anaerobic exercise. Several machine learning algorithms were applied to validate the dataset, with XGBoost achieving an accuracy of 93% in classifying stress versus rest, 91% in distinguishing between aerobic and anaerobic exercise, and 84% in a four-label classification task involving stress, rest, aerobic, and anaerobic activities. The dataset is publicly available for further research.
Fil: Hongn, Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina
Fil: Bosch, Facundo. Instituto Tecnológico de Buenos Aires; Argentina
Fil: Prado, Lara Eleonora. Instituto Tecnológico de Buenos Aires; Argentina
Fil: Ferrández, José Manuel. Universidad Politécnica de Cartagena; España
Fil: Bonomini, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina. Instituto Tecnológico de Buenos Aires; Argentina
description In this work, a novel dataset containing physiological signals recorded non invasevely during structured acute stress induction, as well as aerobic and anaerobic exercise sessions is presented. The physiological data were collected using the Empatica E4, a wearable device that measures electrodermal activity, skin temperature, three-axis accelerometry and blood volume pulse, from which heart rate and heart rate variability features can be derived. A stress induction protocol was designed using mathematical and emotional tasks to elicit physiological responses. For aerobic and anaerobic exercise, a stationary bike routine was developed to distinguish between the two types of activity. The dataset includes records from 36 healthy individuals during the stress protocol, 30 during aerobic exercise, and 31 during anaerobic exercise. Several machine learning algorithms were applied to validate the dataset, with XGBoost achieving an accuracy of 93% in classifying stress versus rest, 91% in distinguishing between aerobic and anaerobic exercise, and 84% in a four-label classification task involving stress, rest, aerobic, and anaerobic activities. The dataset is publicly available for further research.
publishDate 2025
dc.date.none.fl_str_mv 2025-03
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 http://hdl.handle.net/11336/263256
Hongn, Andrea; Bosch, Facundo; Prado, Lara Eleonora; Ferrández, José Manuel; Bonomini, Maria Paula; Wearable physiological signals under acute stress and exercise conditions; Springer; Scientific Data; 12; 1; 3-2025; 1-10
2052-4463
CONICET Digital
CONICET
url http://hdl.handle.net/11336/263256
identifier_str_mv Hongn, Andrea; Bosch, Facundo; Prado, Lara Eleonora; Ferrández, José Manuel; Bonomini, Maria Paula; Wearable physiological signals under acute stress and exercise conditions; Springer; Scientific Data; 12; 1; 3-2025; 1-10
2052-4463
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41597-025-04845-9
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41597-025-04845-9
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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