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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/118871


    Title: BIA: Behavior Identification Algorithm Using Unsupervised Learning Based on Sensor Data for Home Elderly
    Authors: Shang, Cuijuan;Chang, Chih-Yung;Chen, Guilin;Zhao, Shenghui;Chen, Haibao
    Keywords: Senior citizens;Hidden Markov models;Smart homes;Histograms;Clustering algorithms;Unsupervised learning;Probabilistic logic
    Date: 2019-09-24
    Issue Date: 2020-07-07 12:10:25 (UTC+8)
    Abstract: Behavior identification plays an important role in supporting homecare for the elderly living alone. In literature, plenty of algorithms have been designed to identify behaviors of the elderly by learning features or extracting patterns from sensor data. However, most of them adopted probabilistic models or supervised learning to identify behaviors based on labeled sensor data. This paper proposes a behavior identification algorithm ( BIA ) using unsupervised learning based on unlabeled sensor data for the elderly living alone in smart home. This paper presents the observation of elder behaviors with three features: Event Order , Time Length Similarity and Time Interval Similarity features. Based on these features of behavior observations, two properties of behaviors, including the Event Shift and Histogram Shape Similarity properties, are presented. According to these properties, the proposed BIA is developed. Finally, performance results show that the proposed BIA outperforms the existing unsupervised machine learning mechanisms in terms of the behavior identification precision and recall.
    Relation: IEEE Journal of Biomedical and Health Informatics 24(6), p.1589-1600
    DOI: 10.1109/JBHI.2019.2943391
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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