amsys 15(6): e2

Research Article

RF-Care: Device-Free Posture Recognition for Elderly People Using A Passive RFID Tag Array

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  • @ARTICLE{10.4108/eai.22-7-2015.2260064,
        author={Lina Yao and Quan Z. Sheng and Wenjie Ruan and Tao Gu and Xue Li and Nick Falkner and Zhi Yang},
        title={RF-Care: Device-Free Posture Recognition for Elderly People Using A Passive RFID Tag Array},
        journal={EAI Endorsed Transactions on Ambient Systems},
        volume={2},
        number={6},
        publisher={EAI},
        journal_a={AMSYS},
        year={2015},
        month={8},
        keywords={activity recognition, device-free, passive rfid, posture detec- tion, posture transition},
        doi={10.4108/eai.22-7-2015.2260064}
    }
    
  • Lina Yao
    Quan Z. Sheng
    Wenjie Ruan
    Tao Gu
    Xue Li
    Nick Falkner
    Zhi Yang
    Year: 2015
    RF-Care: Device-Free Posture Recognition for Elderly People Using A Passive RFID Tag Array
    AMSYS
    EAI
    DOI: 10.4108/eai.22-7-2015.2260064
Lina Yao1,*, Quan Z. Sheng1, Wenjie Ruan1, Tao Gu2, Xue Li3, Nick Falkner1, Zhi Yang1
  • 1: The University of Adelaide
  • 2: RMIT University
  • 3: The University of Queensland
*Contact email: lina.yao@adelaide.edu.au

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, device-free 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.