Energy-based decision engine for household human activity recognition

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dc.cclicenceCC-BY-NCen
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.acceptance2017-12-23en
dc.date.accessioned2018-01-25T12:28:38Z
dc.date.available2018-01-25T12:28:38Z
dc.date.issued2018-03
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.funderEUen
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.doihttps://doi.org/10.1109/percomw.2018.8480314
dc.identifier.urihttp://hdl.handle.net/2086/15116
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectidMarie Skłodowska-Curie 676157 (ACROSSING), innovation actions 723059 (enCOMPASS)en
dc.publisherIEEEen
dc.researchgroupCIIRGen
dc.researchinstituteCyber Technology Institute (CTI)en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.researchinstituteInstitute of Engineering Sciences (IES)en
dc.subjectActivity recognitionen
dc.subjectmachine learningen
dc.subjectenergyen
dc.titleEnergy-based decision engine for household human activity recognitionen
dc.typeConferenceen

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