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    Passive RFID Module with LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient Assisted Living

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    Accepted manuscript (922.2Kb)
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    Publication date
    2021
    Author
    Oguntala, George A.
    Hu, Yim Fun
    Alabdullah, Ali A.S.
    Abd-Alhameed, Raed A.
    Ali, Muhammad
    Luong, D.K.
    Keyword
    Activity recognition
    Ambient assisted living
    Computational complexity
    Computational modeling
    Computer architecture
    Data models
    LSTM
    Recurrent neural network
    Recurrent neural networks
    RFID
    Senior citizens
    Sensors
    Smart homes
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    Rights
    © 2021 IEEE. Reproduced in accordance with the publisher's self-archiving policy.
    Peer-Reviewed
    Yes
    Open Access status
    Green
    
    Metadata
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    Abstract
    IEEE Human activity recognition from sensor data is a critical research topic to achieve remote health monitoring and ambient assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed to support targets capabilities through digital environments that are sensitive, responsive and adaptive to human activities. Emerging technological paradigms to support AAL within the home or community setting offers people the prospect of a more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework that augments information from the received signal strength indicator (RSSI) of passive RFID tags to obtain detailed activity profiling is proposed. Key indices of position, orientation, mobility, and degree of activities which are critical to guide reliable clinical management decisions using 4 volunteers are employed to simulate the research objective. A two-layer, fully connected sequence long short-term memory recurrent neural network model (LSTM RNN) is employed. The LSTM RNN model extracts the feature of RSS from the sensor data and classifies the sampled activities using SoftMax. The performance of the LSTM model is evaluated for different data size and the hyper-parameters of the RNN are adjusted to optimal states, which results in an accuracy of 98.18%. The proposed framework suits well for smart health and smart homes which offers pervasive sensing environment for the elderly, persons with disability and chronic illness.
    URI
    http://hdl.handle.net/10454/18418
    Version
    Accepted manuscript
    Citation
    Oguntala GA, Hu YF, Alabdullah AAS et al (2021) Passive RFID Module with LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient Assisted Living. IEEE Internet of Things Journal. Accepted for publication.
    Link to publisher’s version
    https://ieeexplore.ieee.org/document/9328564
    Type
    Article
    Collections
    Engineering and Informatics Publications

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