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Towards Human-Centric Psychomotor Recommender Systems

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dc.contributor.authorPortaz Collado, Miguel Ángel
dc.contributor.authorManjarrés Riesco, Ángeles
dc.contributor.authorSantos, Olga C.
dc.contributor.funderAgencia Estatal de Investigación (España)
dc.coverage.spatialLimassol Cyprus
dc.coverage.temporal2023-06-26
dc.date.accessioned2026-01-14T11:14:47Z
dc.date.available2026-01-14T11:14:47Z
dc.date.issued2023-06-16
dc.descriptionThe registered version of this conference paper, first published in "Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (pp. 337–342).", is available online at the publisher's website: https://doi.org/10.1145/3563359.3596993
dc.description.abstractRecommender Systems have been developed for years to guide the interaction of the users with systems in very diverse domains where information overload exists aimed to help humans in decision making. In order to better support the humans, the more the system knows about the user, the more useful recommendations the user can receive. In this sense, there is a need to explore which are the intrinsic human aspects that should be taken into account in each case when building the user models that provide the personalization. Moreover, there is a need to define and apply methodologies, guidelines and frameworks to develop this kind of systems in order to tackle the challenges of current artificial intelligence applications including issues such as ethics, transparency, explainability and sustainability. For our research, we have chosen the psychomotor domain. To provide some insights into this problem, in this paper we present the research directions we are exploring to apply a human-centric approach when developing the iBAID (intelligent Basket AID) psychomotor system, which aims to recommend the physical activities and movements to perform when training in basketball, either to improve the technique, to recover from an injury or even to keep active when getting older.en
dc.description.provenanceSubmitted by Óscar Soto López (osoto@pas.uned.es) on 2026-01-14T11:14:17Z workflow start=Step: editstep - action:claimaction No. of bitstreams: 1 ManjarresRiesco_Angeles_HumanCentricPsychomot_ANGELES MANJARRES RI.pdf: 579624 bytes, checksum: 7cceabecd65971664ecf3367015fc514 (MD5)en
dc.description.provenanceStep: editstep - action:editaction Approved for entry into archive by Óscar Soto López(osoto@pas.uned.es) on 2026-01-14T11:14:47Z (GMT)en
dc.description.provenanceMade available in DSpace on 2026-01-14T11:14:47Z (GMT). No. of bitstreams: 1 ManjarresRiesco_Angeles_HumanCentricPsychomot_ANGELES MANJARRES RI.pdf: 579624 bytes, checksum: 7cceabecd65971664ecf3367015fc514 (MD5) Previous issue date: 2023-06-16en
dc.description.sponsorshipThis work is part of the project HUMANAID (TED2021-129485BC1) funded by MCIN/AEI/ 10.13039/501100011033 and the European Union "NextGenerationEU"/PRTR.
dc.description.versionversión publicada
dc.identifier.citationPortaz, M., Manjarrés, Á., & Santos, O. C. (2023, June). Towards human-centric psychomotor recommender systems. In Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (pp. 337–342). https://doi.org/10.1145/3563359.3596993
dc.identifier.doihttps://doi.org/10.1145/3563359.3596993
dc.identifier.issn9781450398916
dc.identifier.urihttps://hdl.handle.net/20.500.14468/31405
dc.language.isoen
dc.publisherAssociation for Computing Machinery, Inc
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.congressUMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
dc.relation.departmentInteligencia Artificial
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Proyectos Estratégicos Orientados a la Transición Ecológica y a la Transición Digital 2021/TED2021-129485B-C1
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/deed.es
dc.subject1203.04 Inteligencia artificial
dc.subject.keywordsrecommender systemsen
dc.subject.keywordspsychomotor intelligent systemsen
dc.subject.keywordshumancentric systemsen
dc.subject.keywordshybrid artificial intelligenceen
dc.subject.odsODS 3 - Salud y bienestar
dc.subject.odsODS 4 - Educación de calidad
dc.titleTowards Human-Centric Psychomotor Recommender Systemsen
dc.typeactas de congresoes
dc.typeconference proceedingsen
dspace.entity.typePublication
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relation.isAuthorOfPublicationf1dc37a9-f950-4aa8-b83c-632da9961d8d
relation.isAuthorOfPublicationdf3339e5-d482-4ea3-85ad-3a554c2ba075
relation.isAuthorOfPublication.latestForDiscoveryf7ce5008-064f-4b12-b17f-7f70ba978afe
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