A comparative analysis of human behavior prediction approaches in intelligent environments

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dc.contributor.authorAlmeida, Aitor
dc.contributor.authorBermejo Fernández, Unai
dc.contributor.authorBilbao Jayo, Aritz
dc.contributor.authorAzkune Galparsoro, Gorka
dc.contributor.authorAguilera, Unai
dc.contributor.authorEmaldi, Mikel
dc.contributor.authorDornaika, Fadi
dc.contributor.authorArganda-Carreras, Ignacio
dc.date.accessioned2025-05-12T08:16:56Z
dc.date.available2025-05-12T08:16:56Z
dc.date.issued2022-01-18
dc.date.updated2025-05-12T08:16:56Z
dc.description.abstractBehavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modelingen
dc.description.sponsorshipThis work was carried out with the financial support of FuturAAL-Ego (RTI2018-101045-A-C22) and FuturAAL-Context (RTI2018-101045-B-C21) granted by Spanish Ministry of Science, Innovation and Universitiesen
dc.identifier.citationAlmeida, A., Bermejo, U., Bilbao, A., Azkune, G., Aguilera, U., Emaldi, M., Dornaika, F., & Arganda-Carreras, I. (2022). A comparative analysis of human behavior prediction approaches in intelligent environments. Sensors, 22(3). https://doi.org/10.3390/S22030701
dc.identifier.doi10.3390/S22030701
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2716
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2022 by the authors
dc.subject.otherAttention
dc.subject.otherBehavior modeling
dc.subject.otherConvolutional neural networks
dc.subject.otherEmbeddings
dc.subject.otherGraph neural networks
dc.subject.otherIntelligent environments
dc.subject.otherKnowledge graphs
dc.subject.otherRecurrent neural networks
dc.subject.otherTransformers
dc.subject.otherUser behavior prediction
dc.titleA comparative analysis of human behavior prediction approaches in intelligent environmentsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue3
oaire.citation.titleSensors
oaire.citation.volume22
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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