Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains

Detalhes bibliográficos
Autor(a) principal: Gamboa, Patrícia
Data de Publicação: 2022
Outros Autores: Varandas, Rui, Rodrigues, João, Cepeda, Cátia, Quaresma, Cláudia, Gamboa, Hugo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/142797
Resumo: Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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spelling Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domainsattentionbiosignalscognitive taskselectrocardiogramelectroencephalogrammachine learningoccupational healthHuman-Computer InteractionComputer Networks and CommunicationsSDG 3 - Good Health and Well-beingPublisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Occupational disorders considerably impact workers’ quality of life and organizational productivity, and even affect mortality worldwide. Such health issues are related to mental health and ergonomics risk factors. In particular, mental health may be affected by cognitive strain caused by unexpected interruptions and other attention compromising factors. Risk factors assessment associated with cognitive strain in office environments, namely related to attention states, still suffers from the lack of scientifically validated tools. In this work, we aim to develop a series of classification models that can classify attention during pre-defined cognitive tasks based on the acquisition of biosignals to create a ground truth of attention. Biosignals, such as electrocardiography, electroencephalography, and functional near-infrared spectroscopy, were acquired from eight subjects during standard cognitive tasks inducing attention. Individually tuned machine learning models trained with those biosignals allowed us to successfully detect attention on the individual level, with results in the range of 70–80%. The electroencephalogram and electrocardiogram were revealed to be the most appropriate sensors in this context, and the combination of multiple sensors demonstrated the importance of using multiple sources. These models prove to be relevant for the development of attention identification tools by providing ground truth to determine which human–computer interaction variables have strong associations with attention.LIBPhys-UNLRUNGamboa, PatríciaVarandas, RuiRodrigues, JoãoCepeda, CátiaQuaresma, CláudiaGamboa, Hugo2022-08-02T22:25:24Z2022-03-242022-03-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article18application/pdfhttp://hdl.handle.net/10362/142797engPURE: 45155193https://doi.org/10.3390/computers11040049info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:20:42Zoai:run.unl.pt:10362/142797Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:50:31.506833Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains
title Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains
spellingShingle Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains
Gamboa, Patrícia
attention
biosignals
cognitive tasks
electrocardiogram
electroencephalogram
machine learning
occupational health
Human-Computer Interaction
Computer Networks and Communications
SDG 3 - Good Health and Well-being
title_short Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains
title_full Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains
title_fullStr Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains
title_full_unstemmed Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains
title_sort Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains
author Gamboa, Patrícia
author_facet Gamboa, Patrícia
Varandas, Rui
Rodrigues, João
Cepeda, Cátia
Quaresma, Cláudia
Gamboa, Hugo
author_role author
author2 Varandas, Rui
Rodrigues, João
Cepeda, Cátia
Quaresma, Cláudia
Gamboa, Hugo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv LIBPhys-UNL
RUN
dc.contributor.author.fl_str_mv Gamboa, Patrícia
Varandas, Rui
Rodrigues, João
Cepeda, Cátia
Quaresma, Cláudia
Gamboa, Hugo
dc.subject.por.fl_str_mv attention
biosignals
cognitive tasks
electrocardiogram
electroencephalogram
machine learning
occupational health
Human-Computer Interaction
Computer Networks and Communications
SDG 3 - Good Health and Well-being
topic attention
biosignals
cognitive tasks
electrocardiogram
electroencephalogram
machine learning
occupational health
Human-Computer Interaction
Computer Networks and Communications
SDG 3 - Good Health and Well-being
description Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
publishDate 2022
dc.date.none.fl_str_mv 2022-08-02T22:25:24Z
2022-03-24
2022-03-24T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/142797
url http://hdl.handle.net/10362/142797
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 45155193
https://doi.org/10.3390/computers11040049
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 18
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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