Affiliations: Department of Computer Science and Information
Engineering, Tamkang University Taipei, Taiwan
Note: [] Corresponding author: Department of Computer Science and
Information Engineering, Tamkang University, 151 Ying-Chuan Rd. Tamsui, Taipei,
25137, Taiwan. Tel.: +886 2 26215656 Ext. 3307; Fax: +886 2 26209749; E-mail:
[email protected]
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.
Keywords: RFID, behavior modeling, anomaly detection, elderly care, clustering
