Deep Learning for Ground Reaction Force Data Analysis Application to Wide-Area Floor Sensing

Abdullah Alharthi, Krikor Ozanyan

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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    Deep learning methods are proposed to process and
    fuse raw spatiotemporal ground reaction forces (GRF) to
    accurately categorize gait pattern. These methods are based on
    convolutional neural network and long short-term memory
    networks architectures to learn spatiotemporal features,
    automatically end-to-end from raw GRF sensor signals. In a case
    study on Parkinson's disease (PD) data, spatiotemporal signals of
    gait for PD patient and healthy subjects are processed and
    classified, resulting an effective gait pattern classification with a
    precision performance of 96%. Deep learning considerably
    achieved better classification results, compared to the shallow
    learning methods with the handcrafted features. This implies that
    for the purpose of automatic decision-making, it is beneficial to
    utilize deep learning methods to analyse GRF. This insight is
    portable across a range of industrial tasks that involve complex
    spatiotemporal GRF signals classification. The proposed models
    are computationally efficient and able to achieve high
    classification precision from a large set of GRF signals.
    Original languageEnglish
    Title of host publicationThe 28th International Symposium on Industrial Electronics (ISIE)
    Place of PublicationVancouver, BC, Canada
    Number of pages6
    Publication statusPublished - 2019


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