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Multioccupancy activity recognition based on deep learning models fusing UWB localization heatmaps and nearby-sensor interaction

2025-06-01Journal article Open access
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dc.contributor.authorAnguita-Molina, Miguel Ángel
dc.contributor.authorCardoso, Pedro
dc.contributor.authorRodrigues, Joao
dc.contributor.authorMedina-Quero, Javier
dc.contributor.authorPolo-Rodríguez, Aurora
dc.date.accessioned2025-06-26T12:19:19Z
dc.date.available2025-06-26T12:19:19Z
dc.date.issued2025-06-01
dc.description.abstractHuman activity recognition (HAR) focuses on developing systems and techniques to recognize and categorize human actions automatically based on sensor data. This study combines ultrawideband (UWB) technology and binary sensors to describe and recognize daily activities in real-world environments with multiple occupants, ensuring accurate user localization through noninvasive and privacy-respecting methods. A novel method that combines wearables with UWB technology, which allows the generation of heatmaps for accurate positioning, and binary sensors, which collect nearby interaction with daily activities in naturalistic conditions, is presented. A dataset composed of real-world data collected from three individuals in a real-life environment (house) was compiled. Advanced deep learning models are implemented to effectively fuse spatiotemporal information, leading to an encouraging performance in recognition of daily activities. The promising results suggest that this approach could be viable for large-scale deployments in future smart environments.eng
dc.identifier.doi10.1109/jiot.2025.3531316
dc.identifier.eissn2372-2541
dc.identifier.issn2327-4662
dc.identifier.urihttp://hdl.handle.net/10400.1/27303
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofIEEE Internet of Things Journal
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectHuman activity recognition (HAR)
dc.subjectLocalization heatmap
dc.subjectMultioccupancy
dc.subjectUltrawideband (UWB)
dc.titleMultioccupancy activity recognition based on deep learning models fusing UWB localization heatmaps and nearby-sensor interactioneng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage16052
oaire.citation.issue11
oaire.citation.startPage16037
oaire.citation.titleIEEE Internet of Things Journal
oaire.citation.volume12
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCardoso
person.familyNameRodrigues
person.givenNamePedro
person.givenNameJoao
person.identifier.ciencia-id5F10-1C37-FE45
person.identifier.ciencia-id8A19-98F7-9914
person.identifier.orcid0000-0003-4803-7964
person.identifier.orcid0000-0002-3562-6025
person.identifier.ridG-6405-2013
person.identifier.scopus-author-id35602693500
person.identifier.scopus-author-id55807461600
relation.isAuthorOfPublication62bebc54-51ee-4e35-bcf5-6dd69efd09e0
relation.isAuthorOfPublication683ba85b-459c-4789-a4ff-a4e2a904b295
relation.isAuthorOfPublication.latestForDiscovery62bebc54-51ee-4e35-bcf5-6dd69efd09e0

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