Variational autoencoders for anomaly detection in the behaviour of the elderly using electricity consumption data

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dc.contributor.author González, Daniel
dc.contributor.author Patricio Guisado, Miguel Ángel
dc.contributor.author Berlanga de Jesús, Antonio
dc.contributor.author Molina López, José Manuel
dc.date.accessioned 2021-11-09T11:19:10Z
dc.date.available 2021-11-09T11:19:10Z
dc.date.issued 2021-06-15
dc.identifier.bibliographicCitation González, D., Patricio, M.A., Berlanga, A., Molina, J.M. (2021). Variational autoencoders for anomaly detection in thebehaviour of the elderly using electricity consumption data. Expert Systems, e12744. https://doi.org/10.1111/exsy.12744
dc.identifier.issn 0266-4720
dc.identifier.uri http://hdl.handle.net/10016/33555
dc.description.abstract According To The World Health Organization, Between 2000 And 2050, The Propor Tion Of The World&#39 S Population Over 60 Will Double, From 11% To 22%. In Absolute Numbers, This Age Group Will Increase From 605 Million To 2 Billion In The Course Of Half A Century. It Is A Reality That Most Of Them Prefer To Live Alone, So It Is Necessary To Look For Mechanisms And Tools That Will Help Them To Improve Their Autonomy. Although In Recent Years, We Have Been Living In A Veritable Explosion Of Domotic Sys Tems That Facilitate People&#39 S Daily Lives, It Is Also True That There Are Not Many Tools Specifically Aimed At This Sector Of The Population. The Aim Of This Paper Is To Present A Potential Solution To The Monitoring Of Activity Of Daily Living In The Least Intrusive Way For People. In This Case, Anomalous Patterns Of Daily Activities Will Be Detected By Analysing The Daily Consumption Of Household Appliances. People Who Live Alone Usu Ally Have A Pattern Of Daily Behaviour In The Use Of Household Appliances (Coffee Machine, Microwave, Television, Etc.). A Neuronal Model Is Proposed For The Detection Of Abnormal Behaviour Based On An Autoencoder Architecture. This Solution Will Be Compared With A Variational Autoencoder To Analyse The Improvements That Can Be Obtained. The Well-Known Dataset Called Uk-Dale Will Be Used To Validate The Proposal.
dc.description.sponsorship V PRICIT (Regional Programme of Research and Technological Innovation); Madrid Government (Comunidad de Madrid-Spain); Universidad Carlos III de Madrid, and Competitiveness (MINECO), Grant/Award Numbers: RTC-2016-5059-8, RTC-2016-5191-8, RTC-2016-5595-2, TEC2017-88048-C2-2-R; Spanish Ministry of Economy; Company MasMovil
dc.format.extent 12
dc.language.iso eng
dc.publisher Wiley Publishing Ltd
dc.rights © 2021 The Authors. Expert Systems published by John Wiley & Sons Ltd
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other ambient assisted living
dc.subject.other anomaly detection
dc.title Variational autoencoders for anomaly detection in the behaviour of the elderly using electricity consumption data
dc.type article
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1111/exsy.12744
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. RTC-2016-5191-8
dc.relation.projectID Gobierno de España. RTC-2016-5595-2
dc.relation.projectID Gobierno de España. RTC-2016-5059-8
dc.relation.projectID Gobierno de España. TEC2017-88048-C2-2-R
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationlastpage 12
dc.identifier.publicationtitle EXPERT SYSTEMS
dc.identifier.uxxi AR/0000028552
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.contributor.funder Universidad Carlos III de Madrid
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