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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/116996

    Title: Implicit Irregularity Detection using Unsupervised Learning on Daily Behaviors
    Authors: Shang, C. J.;Chang, C. Y.;Chen, G. L.;Zhao, S. H.;Lin, J. Z.
    Keywords: Senior citizens;Feature extraction;Biomedical monitoring;Unsupervised learning;Monitoring;Analytical models;Hardware
    Date: 2019-02
    Issue Date: 2019-09-17 12:10:57 (UTC+8)
    Publisher: IEEE Journal of Biomedical and Health Informatics
    Abstract: The irregularity detection of daily behaviors for the
    elderly is an important issue in homecare. Plenty of mechanisms
    have been developed to detect the health condition of the elderly
    based on the explicit irregularity of several biomedical parameters
    or some specific behaviors. However, few researches focus on
    detecting the implicit irregularity involving the combination of
    diverse behaviors, which can assess the cognitive and physical
    wellbeing of elders but cannot be directly identified based on sensor
    data. This paper proposes an Implicit IRregularity Detection (IIRD)
    mechanism, which aims to detect the implicit irregularity by
    developing the unsupervised learning algorithm based on daily
    behaviors. The proposed IIRD mechanism identifies the distance
    and similarity between daily behaviors, which are important
    features to distinguish the regular and irregular daily behaviors and
    detect the implicit irregularity of elderly health condition.
    Performance results show that the proposed IIRD outperforms the
    existing unsupervised machine learning mechanisms in terms of the
    detection accuracy and irregularity recall.
    Relation: IEEE Journal of Biomedical and Health Informatics, p.1-12
    DOI: 10.1109/JBHI.2019.2896976
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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