淡江大學機構典藏:Item 987654321/118870
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/118870


    Title: Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors
    Authors: Shang, Cuijuan;Chang, Chih-Yung;Chen, Guilin;Zhao, Shenghui;Lin, Jiazao
    Keywords: Senior citizens;Feature extraction;Biomedical monitoring;Unsupervised learning;Monitoring;Analytical models;Hardware
    Date: 2019-02
    Issue Date: 2020-07-07 12:10:23 (UTC+8)
    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 research works 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 that 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 24(1), p.131-143
    DOI: 10.1109/JBHI.2019.2896976
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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