Smart home-assisted anomaly detection system for older adults: a deep learning approach with a comprehensive set of daily activities

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dc.contributor.authorCejudo Taramona, Ander
dc.contributor.authorBeristain Iraola, Andoni
dc.contributor.authorAlmeida, Aitor
dc.contributor.authorRebescher, Kristin
dc.contributor.authorMartín Andonegui, Cristina
dc.contributor.authorMacía, Iván
dc.date.accessioned2025-12-01T15:29:59Z
dc.date.available2025-12-01T15:29:59Z
dc.date.issued2025-01-31
dc.date.updated2025-12-01T15:29:59Z
dc.description.abstractSmart homes have the potential to enable remote monitoring of the health and well-being of older adults, leading to improved health outcomes and increased independence. However, current approaches only consider a limited set of daily activities and do not combine data from individuals. In this work, we propose the use of deep learning techniques to model behavior at the population level and detect significant deviations (i.e., anomalies) while taking into account the whole set of daily activities (41). We detect and visualize daily routine patterns, train a set of recurrent neural networks for behavior modelling with next-day prediction, and model errors with a normal distribution to identify significant deviations while considering the temporal component. Clustering of daily routines achieves a silhouette score of 0.18 and the best model obtains a mean squared error in next day routine prediction of 4.38%. The mean number of deviated activities for the anomalies in the train and test set are 3.6 and 3.0, respectively, with more than 60% of anomalies involving three or more deviated activities in the test set. The methodology is scalable and can incorporate additional activities into the analysis.en
dc.description.sponsorshipThis research was funded by the Government of the Basque Country under grant ZE-2021/00003en
dc.identifier.citationCejudo, A., Beristain, A., Almeida, A., Rebescher, K., Martín, C., & Macía, I. (2025). Smart home-assisted anomaly detection system for older adults: a deep learning approach with a comprehensive set of daily activities. Medical and Biological Engineering and Computing, 63(6), 1821-1835. https://doi.org/10.1007/S11517-025-03308-Y
dc.identifier.doi10.1007/S11517-025-03308-Y
dc.identifier.eissn1741-0444
dc.identifier.issn0140-0118
dc.identifier.urihttps://hdl.handle.net/20.500.14454/4510
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rights© The Author(s) 2025
dc.subject.otherAnomaly
dc.subject.otherClustering
dc.subject.otherDeep learning
dc.subject.otherOlder adult
dc.titleSmart home-assisted anomaly detection system for older adults: a deep learning approach with a comprehensive set of daily activitiesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage1835
oaire.citation.issue6
oaire.citation.startPage1821
oaire.citation.titleMedical and Biological Engineering and Computing
oaire.citation.volume63
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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