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    Presence detection from smart home motion sensor datasets: a model

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    Authors
    Oriwoh, Edewede
    Conrad, Marc
    Affiliation
    University of Bedfordshire
    Issue Date
    2016-09-17
    Subjects
    security
    anomaly detection
    machine learning
    activities of daily living
    smart homes
    
    Metadata
    Show full item record
    Other Titles
    XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016
    Abstract
    Effective physical presence detection in Smart Homes (SH) can be very useful for supporting ageing in place and Ambient Assisted Living (AAL) solutions. It can contribute to ensuring peace of mind and sense of comfort for patients, their families and their carers as well as enhancing their overall physical security. Motion sensor data acquired from SH contain rich, contextual information that can be used to infer single- and multi-user presences. When combined and analysed with other information such as time of day, (expected) total number of occupants and location of any additional presence, among others, decisions can be made about whether the presences detected are legitimate or intrusive. This paper develops a presence detection model based on the analysis of a real-life SH Motion Sensor dataset. The paper also investigates the use of the relationships that exist between devices (nodes) and locations (zones) in SH. The premise is that rules can be used to constrain these relationships and, in combination, the relationships and rules can be used to describe a healthy smart home such that any deviation from the defined healthy state will result in an anomaly being flagged by a SH monitoring system.
    Citation
    Oriwoh E., Conrad M. (2016) 'Presence detection from smart home motion sensor datasets: a model', XIV Mediterranean Conference on Medical and Biological Engineering and Computing - Paphos, Springer Verlag.
    Publisher
    Springer Verlag
    URI
    http://hdl.handle.net/10547/624204
    DOI
    10.1007/978-3-319-32703-7_240
    Additional Links
    https://link.springer.com/chapter/10.1007/978-3-319-32703-7_240
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9783319327013
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-32703-7_240
    Scopus Count
    Collections
    Computing

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