IDEAS home Printed from https://ideas.repec.org/a/igg/japuc0/v10y2018i4p1-13.html
   My bibliography  Save this article

At Sensor Diagnosis for Smart Healthcare: Probability or Conditional Probability Based Approach vs. k-Nearest Neighbour

Author

Listed:
  • Chetna Laroiya

    (Jagan Institute of Management Studies (Affiliated to GGSIP University), New Delhi, India)

  • Vijay Bhushan Aggarwal

    (University of Illinois, Urbana, USA)

Abstract

In order to implement IoT-based health-care for improved quality of life, we have to deal with sensor and communication technologies. In this article, the authors propose an approach to analyse real-time data streaming from a patient's surface body sensors, which are to be looked upon in a small sliding window frame. Time series analysis of data from the sensors is effective in reducing the round-trip delay between patient and the medical server. Two algorithms are for the sensor, and odd measures are proposed based on joint probability and joint conditional probability. The proposed algorithms are to be SQL compliant, as traces of at-sensor UDBMS alongside elementary capabilities supports databases with a meagre amount of SQL, which is evident in the literature.

Suggested Citation

  • Chetna Laroiya & Vijay Bhushan Aggarwal, 2018. "At Sensor Diagnosis for Smart Healthcare: Probability or Conditional Probability Based Approach vs. k-Nearest Neighbour," International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC), IGI Global, vol. 10(4), pages 1-13, October.
    • Handle: RePEc:igg:japuc0:v:10:y:2018:i:4:p:1-13
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJAPUC.2018100101
    Download Restriction: no
    ---><---

    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:japuc0:v:10:y:2018:i:4:p:1-13. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.