淡江大學機構典藏:Item 987654321/116996
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    題名: Implicit Irregularity Detection using Unsupervised Learning on Daily Behaviors
    作者: Shang, C. J.;Chang, C. Y.;Chen, G. L.;Zhao, S. H.;Lin, J. Z.
    關鍵詞: Senior citizens;Feature extraction;Biomedical monitoring;Unsupervised learning;Monitoring;Analytical models;Hardware
    日期: 2019-02
    上傳時間: 2019-09-17 12:10:57 (UTC+8)
    出版者: IEEE Journal of Biomedical and Health Informatics
    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 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.
    關聯: IEEE Journal of Biomedical and Health Informatics, p.1-12
    DOI: 10.1109/JBHI.2019.2896976
    顯示於類別:[資訊工程學系暨研究所] 期刊論文

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