Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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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 |
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1799138101940977664 |