IDEAS home Printed from https://ideas.repec.org/a/igg/jdst00/v14y2023i2p1-12.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDST.317942
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yiğit Kazançoğlu & Muhittin Sağnak & Çisem Lafcı & Sunil Luthra & Anil Kumar & Caner Taçoğlu, 2021. "Big Data-Enabled Solutions Framework to Overcoming the Barriers to Circular Economy Initiatives in Healthcare Sector," IJERPH, MDPI, vol. 18(14), pages 1-21, July.
    2. Yu Cao & Liyan Huang & Nur Mardhiyah Aziz & Syahrul Nizam Kamaruzzaman, 2022. "Building Information Modelling (BIM) Capabilities in the Design and Planning of Rural Settlements in China: A Systematic Review," Land, MDPI, vol. 11(10), pages 1-34, October.
    3. Rahman, Mohammad Saidur & Khalil, Ibrahim & Yi, Xun, 2019. "A lossless DNA data hiding approach for data authenticity in mobile cloud based healthcare systems," International Journal of Information Management, Elsevier, vol. 45(C), pages 276-288.
    4. Ali, Omar & Murray, Peter A. & Momin, Mujtaba & Dwivedi, Yogesh K. & Malik, Tegwen, 2024. "The effects of artificial intelligence applications in educational settings: Challenges and strategies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    5. Issa Helmi & Lakkis Hussein & Dakroub Roy & Jaber Jad, 2023. "Examining User Engagement and Experience in Agritech," International Journal of Contemporary Management, Sciendo, vol. 59(2), pages 17-32, June.
    6. Raimo, Nicola & De Turi, Ivano & Albergo, Francesco & Vitolla, Filippo, 2023. "The drivers of the digital transformation in the healthcare industry: An empirical analysis in Italian hospitals," Technovation, Elsevier, vol. 121(C).
    7. Ariyaluran Habeeb, Riyaz Ahamed & Nasaruddin, Fariza & Gani, Abdullah & Targio Hashem, Ibrahim Abaker & Ahmed, Ejaz & Imran, Muhammad, 2019. "Real-time big data processing for anomaly detection: A Survey," International Journal of Information Management, Elsevier, vol. 45(C), pages 289-307.
    8. Ali, Omar & Momin, Mujtaba & Shrestha, Anup & Das, Ronnie & Alhajj, Fadia & Dwivedi, Yogesh K., 2023. "A review of the key challenges of non-fungible tokens," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    9. Ali, Omar & Murray, Peter A. & Momin, Mujtaba & Al-Anzi, Fawaz S., 2023. "The knowledge and innovation challenges of ChatGPT: A scoping review," Technology in Society, Elsevier, vol. 75(C).
    10. Imran Ali & Devika Kannan, 2022. "Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review," Annals of Operations Research, Springer, vol. 315(1), pages 29-55, August.
    11. Alessio Luschi & Giovanni Luca Daino & Gianpaolo Ghisalberti & Vincenzo Mezzatesta & Ernesto Iadanza, 2024. "Empowering Clinical Engineering and Evidence-Based Maintenance with IoT and Indoor Navigation," Future Internet, MDPI, vol. 16(8), pages 1-21, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jdst00:v:14:y:2023:i:2:p:1-12. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.