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    Towards falls prevention: A wearable wireless and battery-less sensing and automatic identification tag for real time monitoring of human movements.

    Access Status
    Fulltext not available
    Authors
    Ranasinghe, Damith
    Shinmoto Torres, Roberto
    Sample, Alanson
    Smith, Joshua
    Hill, Keith
    Visvanathan, Renuka
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Ranasinghe, Damith and Shinmoto Torres, Roberto and Sample, Alanson and Smith, Joshua and Hill, Keith and Visvanathan, Renuka. 2012. Towards falls prevention: A wearable wireless and battery-less sensing and automatic identification tag for real time monitoring of human movements, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): pp. 6402-6405. San Diego, CA: IEEE.
    Source Title
    Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
    DOI
    10.1109/EMBC.2012.6347459
    ISSN
    1557 170X
    URI
    http://hdl.handle.net/20.500.11937/29390
    Collection
    • Curtin Research Publications
    Abstract

    Falls related injuries among elderly patients in hospitals or residents in residential care facilities is a significant problem that causes emotional and physical trauma to those involved while presenting a rising healthcare expense in countries such as Australia where the population is ageing. Novel approaches using low cost and privacy preserving sensor enabled Radio Frequency Identification (RFID) technology may have the potential to provide a low cost and effective technological intervention to prevent falls in hospitals. We outline the details of a wearable sensor enabled RFID tag that is battery free, low cost, lightweight, maintenance free and can be worn continuously for automatic and unsupervised remote monitoring of activities of frail patients at acute hospitals or residents in residential care. The technological developments outlined in the paper forms part of an overall technological intervention developed to reduce falls at acute hospitals or in residential care facilities. This paper outlines the details of the technology, underlying algorithms and the results (where an accuracy of 94-100% was achieved) of a successful pilot trial.

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