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Challenges of learning human digital twin: case study of mental wellbeing: Using sensor data and machine learning to create HDT

2023-08-10Journal article Open access
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dc.contributor.authorVildjiounaite, Elena
dc.contributor.authorKallio, Johanna
dc.contributor.authorKantorovitch, Julia
dc.contributor.authorKinnula, Atte
dc.contributor.authorFerreira, Simão
dc.contributor.authorRodrigues, Matilde
dc.contributor.authorRocha, Nuno
dc.date.accessioned2023-12-11T16:08:46Z
dc.date.available2023-12-11T16:08:46Z
dc.date.issued2023-08-10
dc.description.abstractHuman Digital Twin (HDT) is a powerful tool to create a virtual replica of a human, to be used for example for designing interactions with physical systems, preventing cognitive overload, managing human capital, and maintaining a healthy and motivated workforce. Building human twins is a challenging task due to the need to reliably represent each corresponding human being, and the fact that human beings notably differ from each other. Therefore, relying solely on expert knowledge is insufficient, and human twins must learn the specifics of each individual in order to accurately represent them. This paper focuses on AI methods for modelling the mental wellbeing of knowledge workers because the mounting cognitive demands of both white-collar and blue-collar work lead to employees’ stress, and stress leads to diminished creativity and motivation, increased sick leaves, and in severe cases, accidents, burnouts, and disabilities. This paper describes the main building blocks of AI-based detectors of mental stress and highlights the main challenges and future directions of research., which are expected to be relevant also for HDT learning in other domains because the high degree of individuality is ubiquitous in all human activities.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationVildjiounaite, E., Kallio, J., Kantorovitch, J., Kinnula, A., Ferreira, S., Rodrigues, M. A., & Rocha, N. (2023). Challenges of learning human digital twin: Case study of mental wellbeing: Using sensor data and machine learning to create HDT. Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments, 574–583. https://doi.org/10.1145/3594806.3596538pt_PT
dc.identifier.doi10.1145/3594806.3596538pt_PT
dc.identifier.isbn979-8-4007-0069-9/23/07
dc.identifier.urihttp://hdl.handle.net/10400.22/24132
dc.language.isoengpt_PT
dc.publisherACM Digital Librarypt_PT
dc.relation.publisherversionhttps://dl.acm.org/doi/10.1145/3594806.3596538pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectPersonalisationpt_PT
dc.subjectMental wellbeingpt_PT
dc.subjectStresspt_PT
dc.subjectWorkplacept_PT
dc.subjectHuman digital twinpt_PT
dc.titleChallenges of learning human digital twin: case study of mental wellbeing: Using sensor data and machine learning to create HDTpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage583pt_PT
oaire.citation.startPage574pt_PT
oaire.citation.titlePETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environmentspt_PT
person.familyNameFerreira
person.familyNameRodrigues
person.familyNameRocha
person.givenNameSimão
person.givenNameMatilde
person.givenNameNuno
person.identifier2680796
person.identifier.ciencia-idB11E-1BD3-5F34
person.identifier.ciencia-id5110-3A70-C3F3
person.identifier.orcid0000-0001-8233-2217
person.identifier.orcid0000-0001-6175-6934
person.identifier.orcid0000-0002-8472-2603
person.identifier.ridN-7022-2015
person.identifier.scopus-author-id57359736400
person.identifier.scopus-author-id55485977900
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication0bd5b0fc-6849-4f36-b751-87e7f4c81eff
relation.isAuthorOfPublication8ddb200d-027c-40ca-8ca4-7e3a5981bcb1
relation.isAuthorOfPublication7809ef89-8994-4ef1-a1e4-92fe46b0da89
relation.isAuthorOfPublication.latestForDiscovery0bd5b0fc-6849-4f36-b751-87e7f4c81eff

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