Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

Detalhes bibliográficos
Autor(a) principal: Varandas, Rui
Data de Publicação: 2022
Outros Autores: Lima, Rodrigo, Badia, Sergi Bermúdez I., Silva, Hugo, 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/10362/142798
Resumo: Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
id RCAP_e03bb23a5b6c90a2325b4f80c24b8b36
oai_identifier_str oai:run.unl.pt:10362/142798
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learningbrain–computer interfacecognitive fatiguefunctional near-infrared spectroscopymachine learningAnalytical ChemistryInformation SystemsAtomic and Molecular Physics, and OpticsBiochemistryInstrumentationElectrical and Electronic EngineeringSDG 3 - Good Health and Well-beingPublisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67%. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables.LIBPhys-UNLNOVALincsRUNVarandas, RuiLima, RodrigoBadia, Sergi Bermúdez I.Silva, HugoGamboa, Hugo2022-08-02T22:25:33Z2022-05-252022-05-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfhttp://hdl.handle.net/10362/142798eng1424-8220PURE: 45392530https://doi.org/10.3390/s22114010info: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:10362/142798Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:50:31.560889Repositório 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 Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning
title Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning
spellingShingle Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning
Varandas, Rui
brain–computer interface
cognitive fatigue
functional near-infrared spectroscopy
machine learning
Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering
SDG 3 - Good Health and Well-being
title_short Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning
title_full Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning
title_fullStr Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning
title_full_unstemmed Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning
title_sort Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning
author Varandas, Rui
author_facet Varandas, Rui
Lima, Rodrigo
Badia, Sergi Bermúdez I.
Silva, Hugo
Gamboa, Hugo
author_role author
author2 Lima, Rodrigo
Badia, Sergi Bermúdez I.
Silva, Hugo
Gamboa, Hugo
author2_role author
author
author
author
dc.contributor.none.fl_str_mv LIBPhys-UNL
NOVALincs
RUN
dc.contributor.author.fl_str_mv Varandas, Rui
Lima, Rodrigo
Badia, Sergi Bermúdez I.
Silva, Hugo
Gamboa, Hugo
dc.subject.por.fl_str_mv brain–computer interface
cognitive fatigue
functional near-infrared spectroscopy
machine learning
Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering
SDG 3 - Good Health and Well-being
topic brain–computer interface
cognitive fatigue
functional near-infrared spectroscopy
machine learning
Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering
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 2022-08-02T22:25:33Z
2022-05-25
2022-05-25T00:00:00Z
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/10362/142798
url http://hdl.handle.net/10362/142798
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1424-8220
PURE: 45392530
https://doi.org/10.3390/s22114010
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 14
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
_version_ 1799138101944123392

Registros relacionados