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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/58499


    Title: RFID-Based Human Behavior Modeling and Anomaly Detection for Elderly Care
    Other Titles: 基於RFID的人類行為塑模與異常偵測於老人照護
    Authors: Hsu, Hui-Huang;Chen, Chien-Chen
    Contributors: 淡江大學資訊工程學系
    Keywords: RFID;behavior modeling;anomaly detection;elderly care;clustering
    Date: 2010-12
    Issue Date: 2011-10-01 12:01:05 (UTC+8)
    Publisher: Amsterdam: I O S Press
    Abstract: This research aimed at building an intelligent system that can detect abnormal behavior for the elderly at home. Active RFID tags can be deployed at home to help collect daily movement data of the elderly who carries an RFID reader. When the reader detects the signals from the tags, RSSI values that represent signal strength are obtained. The RSSI values are reversely related to the distance between the tags and the reader and they are recorded following the movement of the user. The movement patterns, not the exact locations, of the user are the major concern. With the movement data (RSSI values), the clustering technique is then used to build a personalized model of normal behavior. After the model is built, any incoming datum outside the model can be viewed as abnormal and an alarm can be raised by the system. In this paper, we present the system architecture for RFID data collection and preprocessing, clustering for anomaly detection, and experimental results. The results show that this novel approach is promising.
    本研究的目的在於構立一個可以自動偵測在家老人異常行為的智慧型系統。我們將主動式RFID標籤(tag)家裡固定的位置,以收集攜帶RFID讀取器(reader)的老人在家移動的資料。當讀取器偵測到來自主動式標籤的訊號,我們就可以獲得代表訊號強度的RSSI值。RSSI值與讀取器和標籤之間距離是負向的關係,當使用者移動時這些值就會隨著被記錄下來。我們關心的並不是使用者確切的位置,而是其移動的模式。有了這些使用者移動的資料(RSSI值),我們再利用分群(clustering)的技術來建立一個人化的正常行為模型。當這樣的模型備是當地建立起來之後,任何若在模型以外的新進資料被視為是異常,而系統可以發出警報來警告使用者的照護者或子女。在這篇論文中,我們介紹了系統架構、RFID資料的收集和前處理、分群技術於異常偵測、以及實驗的結果。實驗的結果證明這樣的創新作法是可行的。
    Relation: Mobile Information Systems 6(4), pp.341-354
    DOI: 10.3233/MIS-2010-0107
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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