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dc.contributor.authorCasagrande, Flavia Dias
dc.contributor.authorZouganeli, Evi
dc.date.accessioned2019-01-17T13:38:54Z
dc.date.accessioned2019-06-04T11:45:41Z
dc.date.available2019-01-17T13:38:54Z
dc.date.available2019-06-04T11:45:41Z
dc.date.issued2018
dc.identifier.citationCasagrande, F.D. & Zouganeli, E. P. (2018). Occupancy and daily activity event modelling in smart homes for older adults with mild cognitive impairment or dementia. I L.E. Øi, T.M. Komulainen, R.T. Bye & L.O. Nord (Red.), Proceedings of The 59th Conference on Simulation and Modelling (SIMS 59), 26-28 September 2018, Oslo Metropolitan University, Norway, (s. 236-242).en
dc.identifier.isbn978-91-7685-494-5
dc.identifier.issn1650-3740
dc.identifier.issn1650-3686
dc.identifier.issn1650-3740
dc.identifier.urihttps://hdl.handle.net/10642/7194
dc.description.abstractIn this paper we present event anticipation and prediction of sensor data in a smart home environment with a limited number of sensors. Data is collected from a real home with one resident. We apply two state-of-the-art Markov based prediction algorithms − Active LeZi and SPEED − and analyse their performance with respect to a number of parameters, including the size of the training and testing set, the size of the prediction window, and the number of sensors. The model is built based on a training dataset and subsequently tested on a separate test dataset. An accuracy of 75% is achieved when using SPEED while 53% is achieved when using Active LeZi.en
dc.language.isoenen
dc.publisherLinköping University Electronic Pressen
dc.relation.ispartofseriesLinköping Electronic Conference Proceedings;153
dc.subjectSmart home
dc.subjectPrediction models
dc.subjectSensor data
dc.subjectOccupancy modelling
dc.subjectEvent modelling
dc.titleOccupancy and daily activity event modelling in smart homes for older adults with mild cognitiveiImpairment or dementiaen
dc.typeChapteren
dc.typePeer revieweden
dc.date.updated2019-01-17T13:38:54Z
dc.description.versionacceptedVersionen
dc.identifier.doihttp://doi.org/10.3384/ecp18153236
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dc.relation.projectIDNorges forskningsråd: 247620
dc.source.isbn978-91-7685-494-5


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