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dc.contributor.authorYe, Juan
dc.contributor.authorStevenson, Graeme
dc.contributor.authorDobson, Simon
dc.date.accessioned2017-06-29T23:33:52Z
dc.date.available2017-06-29T23:33:52Z
dc.date.issued2016-12
dc.identifier.citationYe , J , Stevenson , G & Dobson , S 2016 , ' Detecting abnormal events on binary sensors in smart home environments ' Pervasive and Mobile Computing , vol. 33 , pp. 32-49 . https://doi.org/10.1016/j.pmcj.2016.06.012en
dc.identifier.issn1574-1192
dc.identifier.otherPURE: 243605797
dc.identifier.otherPURE UUID: a6dbab3f-9b16-4c47-972f-e6b411052edc
dc.identifier.otherScopus: 84979502175
dc.identifier.urihttp://hdl.handle.net/10023/11120
dc.description.abstractWith a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies.
dc.format.extent23
dc.language.isoeng
dc.relation.ispartofPervasive and Mobile Computingen
dc.rightsCopyright © 2016 Elsevier B.V. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://dx.doi.org/10.1016/j.pmcj.2016.06.012en
dc.subjectOntologiesen
dc.subjectDomain knowledgeen
dc.subjectSmart homeen
dc.subjectFault detectionen
dc.subjectSemanticsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRA0421 Public health. Hygiene. Preventive Medicineen
dc.subjectT Technology (General)en
dc.subjectNDASen
dc.subject.lccQA75en
dc.subject.lccRA0421en
dc.subject.lccT1en
dc.titleDetecting abnormal events on binary sensors in smart home environmentsen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1016/j.pmcj.2016.06.012
dc.description.statusPeer revieweden
dc.date.embargoedUntil2017-06-29


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