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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/118870

    题名: Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors
    作者: Shang, Cuijuan;Chang, Chih-Yung;Chen, Guilin;Zhao, Shenghui;Lin, Jiazao
    关键词: Senior citizens;Feature extraction;Biomedical monitoring;Unsupervised learning;Monitoring;Analytical models;Hardware
    日期: 2019-02
    上传时间: 2020-07-07 12:10:23 (UTC+8)
    摘要: 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.
    關聯: IEEE Journal of Biomedical and Health Informatics 24(1), p.131-143
    DOI: 10.1109/JBHI.2019.2896976
    显示于类别:[資訊工程學系暨研究所] 期刊論文


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