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Affective computing emotional body gesture recognition: evolution and the cream of the crop

2025Artigo científico Acesso aberto
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dc.contributor.authorMigueis Vaz Martins, Pedro Jorge
dc.contributor.authorRodrigues, Joao
dc.contributor.authorCardoso, Pedro
dc.date.accessioned2025-12-30T14:02:11Z
dc.date.available2025-12-30T14:02:11Z
dc.date.issued2025
dc.description.abstractThe field of affective computing (AffC) has experienced significant growth, making it challenging to stay up to date with the latest advancements. This surge in interest has likely contributed to a significant rise in the number of systematic reviews or surveys (SRoS) being published across various journals, covering topics like databases, methods, and general perspectives. This paper provides three key contributions: 1) A comprehensive analysis of the evolution of emotion recognition methods from 2002 to 2024, with particular emphasis on emotional body gesture recognition, documenting a clear transition from traditional machine learning to sophisticated deep learning architectures; 2) Identification and detailed analysis of the most impactful papers (the ‘‘cream of the crop’’) that have shaped body-based AffC methods, revealing that modern approaches increasingly use attention mechanisms, graph-based representations for skeletal data, and advanced spatial-temporal modeling techniques; and 3) A systematic categorization and analysis of emotion recognition methods across architectural types (machine learning, deep learning, and hybrid) and modalities (emotional body gesture recognition, facial emotion recognition, multimodal emotion recognition, and speech emotion recognition), demonstrating the field’s progression from unimodal to more robust multimodal approaches. Through an analysis of 10 selected SRoS papers published between 2021-2024, referencing 292 papers collectively, this study reveals critical challenges including limited availability of large-scale body-based emotional databases, computational demands of modern architectures, and cross-database generalization issues.eng
dc.description.sponsorshipThis work was supported by NOVA Laboratory for Computer Science and Informatics (NOVA LINCS) under grant UID/04516/NOVA with the financial support of FCT.IP, and by project AI.EVENT: Monitor Live Audience with AI (ALGARVE-FEDER-01180500, Ref. 17325) co-financed by ALGARVE 2030, Portugal 2030, and the European Union. The work of Pedro J. Vaz was supported by the Portuguese Foundation for Science and Technology (FCT) under PhD grant 2024.04816.BD
dc.identifier.doi10.1109/access.2025.3630563
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10400.1/28022
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.ispartofIEEE Access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAffective computing
dc.subjectBody-based emotion recognition
dc.titleAffective computing emotional body gesture recognition: evolution and the cream of the cropeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FCEC%2F04516%2F2013/PT
oaire.citation.titleIEEE Access
oaire.citation.volume13
oaire.fundingStream5876
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMigueis Vaz Martins
person.familyNameRodrigues
person.familyNameCardoso
person.givenNamePedro Jorge
person.givenNameJoao
person.givenNamePedro
person.identifier.ciencia-id8A19-98F7-9914
person.identifier.ciencia-id5F10-1C37-FE45
person.identifier.orcid0000-0002-8819-3243
person.identifier.orcid0000-0002-3562-6025
person.identifier.orcid0000-0003-4803-7964
person.identifier.ridHKF-6445-2023
person.identifier.ridG-6405-2013
person.identifier.scopus-author-id55807461600
person.identifier.scopus-author-id35602693500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication41a140ef-1573-4bf7-9603-e5ad5e0aacdf
relation.isAuthorOfPublication683ba85b-459c-4789-a4ff-a4e2a904b295
relation.isAuthorOfPublication62bebc54-51ee-4e35-bcf5-6dd69efd09e0
relation.isAuthorOfPublication.latestForDiscovery41a140ef-1573-4bf7-9603-e5ad5e0aacdf
relation.isProjectOfPublication37bdfcd7-b84f-4d11-8fa5-25a959cd5438
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