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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.
    Relation: Mobile Information Systems 6(4), pp.341-354
    DOI: 10.3233/MIS-2010-0107
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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