PROMPT: PROcess Mining and Paravector Tensor based Physical Health Monitoring Framework

KHOWAJA, Sunder Ali, KHUWAJA, Parus, DEV, Kapal and JARWAR, Muhammad Aslam (2022). PROMPT: PROcess Mining and Paravector Tensor based Physical Health Monitoring Framework. IEEE Sensors Journal, p. 1.

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PROMPT_PROcess_Mining_and_Paravector_Tensor_based_Physical_Health_Monitoring_Framework.pdf - Accepted Version
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Official URL: https://ieeexplore.ieee.org/document/9852145
Link to published version:: https://doi.org/10.1109/jsen.2022.3195613
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    Abstract

    The provision of physical healthcare services during the isolation phase is one of the major challenges associated with the current COVID-19 pandemic. Smart healthcare services face a major challenge in the form of human behavior, which is based on human activities, complex patterns, and subjective nature. Although the advancement in portable sensors and artificial intelligence has led to unobtrusive activity recognition systems but very few studies deal with behavior tracking for addressing the problem of variability and behavior dynamics. In this regard, we propose the fusion of PRocess mining and Paravector Tensor (PROMPT) based physical health monitoring framework that not only tracks subjective human behavior, but also deals with the intensity variations associated with inertial measurement units. Our experimental analysis on a publicly available dataset shows that the proposed method achieves 14.56% better accuracy in comparison to existing works. We also propose a generalized framework for healthcare applications using wearable sensors and the PROMPT method for its triage with physical health monitoring systems in the real world.

    Item Type: Article
    Uncontrolled Keywords: 0205 Optical Physics; 0906 Electrical and Electronic Engineering; 0913 Mechanical Engineering; Analytical Chemistry
    Identification Number: https://doi.org/10.1109/jsen.2022.3195613
    Page Range: p. 1
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 19 Aug 2022 13:55
    Last Modified: 27 Aug 2022 02:01
    URI: http://shura.shu.ac.uk/id/eprint/30620

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