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dc.contributor.authorVafeiadis, Anastasiosen
dc.contributor.authorVafeiadis, Thanasisen
dc.contributor.authorZikos, Steliosen
dc.contributor.authorKrinidis, Steliosen
dc.contributor.authorVotis, Konstantinosen
dc.contributor.authorGiakoumis, Dimitriosen
dc.contributor.authorIoannidis, Dimosthenisen
dc.contributor.authorTzovaras, Dimitriosen
dc.contributor.authorChen, Limingen
dc.contributor.authorHamzaoui, Raoufen
dc.date.accessioned2018-01-25T12:28:38Z
dc.date.available2018-01-25T12:28:38Z
dc.date.issued2018-03
dc.identifier.citationVafeiadis, A. et al. Energy-based decision engine for household human activity recognition, IEEE Int. Conf. Pervasive Computing and Communication Workshops (PerCom Workshops), Athens, March. 2018.en
dc.identifier.urihttp://hdl.handle.net/2086/15116
dc.description.abstractWe propose a framework for energy-based human activity recognition in a household environment. We apply machine learning techniques to infer the state of household appliances from their energy consumption data and use rulebased scenarios that exploit these states to detect human activity. Our decision engine achieved a 99.1% accuracy for real-world data collected in the kitchens of two smart homes.en
dc.language.isoen_USen
dc.publisherIEEEen
dc.subjectActivity recognitionen
dc.subjectmachine learningen
dc.subjectenergyen
dc.titleEnergy-based decision engine for household human activity recognitionen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1109/percomw.2018.8480314
dc.researchgroupCIIRGen
dc.peerreviewedYesen
dc.funderEUen
dc.projectidMarie Skłodowska-Curie 676157 (ACROSSING), innovation actions 723059 (enCOMPASS)en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017-12-23en
dc.researchinstituteCyber Technology Institute (CTI)en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.researchinstituteInstitute of Engineering Sciences (IES)en


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