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    Human activity classification using long short-term memory network

    Access Status
    Fulltext not available
    Authors
    Malshika Welhenge, Anuradhi
    Taparugssanagorn, A.
    Date
    2019
    Type
    Journal Article
    
    Metadata
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    Citation
    Malshika Welhenge, A. and Taparugssanagorn, A. 2019. Human activity classification using long short-term memory network. Signal, Image and Video Processing. 13 (4): pp. 651-656.
    Source Title
    Signal, Image and Video Processing
    DOI
    10.1007/s11760-018-1393-7
    ISSN
    1863-1703
    URI
    http://hdl.handle.net/20.500.11937/90320
    Collection
    • Curtin Research Publications
    Abstract

    Activities of daily living (ADL) can be used to identify a person’s daily routine which helps health professionals to provide preventive healthcare. Classification of ADLs is therefore very important. In this study, long short-term memory (LSTM) network, which is an extension of recurrent neural networks, is used. Data collected in MobiAct data set are used to train and test the network. An accuracy of 0.90 is achieved using LSTM network.

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