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Physiological Big Data Mining Through Machine Learning and Wireless Sensor Networks

Author

Listed:
  • Qianlin Tan

    (Hechi University, China)

  • Xinyou Xu

    (Hechi University, China)

  • Hongjia Liang

    (Hechi University, China)

Abstract

With the improvement of living standards, the requirements for medical care and daily healthcare quality have become higher and higher. However, the traditional medical diagnosis mode cannot provide patients with all-round, real-time, and accurate health status. With the aggravation of the aging population, the scale of physiological data will increase in a blowout manner. The traditional medical diagnosis model for monitoring, which is based at the central hospital, has been unable to meet the current real-time monitoring needs for families and individuals. In order to solve this issue, this paper establishes a wireless sensor network based medical platform, which implements sleep monitoring by mining electroencephalogram signals. The wireless sensor network-based medical platform adopts the end-edge-cloud architecture. The experiments and simulations show the effectiveness of the proposed end-edge-cloud architecture-based medical platform.

Suggested Citation

  • Qianlin Tan & Xinyou Xu & Hongjia Liang, 2023. "Physiological Big Data Mining Through Machine Learning and Wireless Sensor Networks," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 14(2), pages 1-12, January.
    • Handle: RePEc:igg:jdst00:v:14:y:2023:i:2:p:1-12
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    References listed on IDEAS

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    1. Ali, Omar & Shrestha, Anup & Soar, Jeffrey & Wamba, Samuel Fosso, 2018. "Cloud computing-enabled healthcare opportunities, issues, and applications: A systematic review," International Journal of Information Management, Elsevier, vol. 43(C), pages 146-158.
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