Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108662
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Type: Conference paper
Title: RF-care: device-free posture recognition for elderly people using a passive RFID tag array
Author: Yao, L.
Sheng, Q.
Ruan, W.
Gu, T.
Li, X.
Falkner, N.
Yang, Z.
Citation: Proceedings of the 12th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2015 / pp.120-129
Publisher: ICST
Issue Date: 2015
ISBN: 9781631900723
Conference Name: 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MOBIQUITOUS) (22 Jul 2015 - 24 Jul 2015 : Coimbra, Portugal)
Statement of
Responsibility: 
Lina Yao, Quan Z. Sheng, Wenjie Ruan, Tao Gu, Xue Li, Nickolas J.G. Falkner, and Zhi Yang
Abstract: Activity recognition is a fundamental research topic for a wide range of important applications such as fall detection for elderly people. Existing techniques mainly rely on wearable sensors, which may not be reliable and practical in real-world situations since people often forget to wear these sensors. For this reason, devicefree activity recognition has gained the popularity in recent years. In this paper, we propose an RFID (radio frequency identification) based, device-free posture recognition system. More specifically, we analyze Received Signal Strength Indicator (RSSI) signal patterns from an RFID tag array, and systematically examine the impact of tag configuration on system performance. On top of selected optimal subset of tags, we study the challenges on posture recognition. Apart from exploring posture classification, we specially propose to infer posture transitions via Dirichlet Process Gaussian Mixture Model (DPGMM) based Hidden Markov Model (HMM), which effectively captures the nature of uncertainty caused by signal strength varieties during posture transitions. We run a pilot study to evaluate our system with 12 orientation-sensitive postures and a series of posture change sequences. We conduct extensive experiments in both lab and real-life home environments. The results demonstrate that our system achieves high accuracy in both environments, which holds the potential to support assisted living of elderly people.
Keywords: Activity Recognition; Device-Free; Passive RFID; Posture Detection; Posture Transition
Rights: Copyright © 2015 L. Yao et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
RMID: 0030039501
DOI: 10.4108/eai.22-7-2015.2260064
Appears in Collections:Computer Science publications

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