• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • Help
    • Item Submission
    • Publisher policies
    • User guides
    • FAQs
  • About QSpace
    • Vision & Mission
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Computer Science & Engineering
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Computer Science & Engineering
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A Reconfigurable Connected Health Platform Using ZYNQ System on Chip

    Thumbnail
    Date
    2018
    Author
    Abunahia D.G.
    Ismail T.A.
    Abou Al Ola H.R.
    Amira A.
    Ait Si Ali A.
    Bensaali F.
    ...show more authors ...show less authors
    Metadata
    Show full item record
    Abstract
    This paper presents a reconfigurable connected health platform for fall and cardiac disease detection to be used in smart home environments using Shimmer sensing device and ZYNQ System on Chip (SoC) platform. The system can also be deployed in ambulances to equip them with health monitoring technologies. The system is designed to be used by elderly, diabetics, muscular and neurological patients who are likely to fall, in addition to heart patients who are probable to get heart attacks causing falls. This project aims to provide users and their families with a sense of mental and physical security in their houses. It will also help them pass through the abstraction of money, since they will not need costly home care nursing services, and they will enjoy relief without having an observer, hence providing a significant socioeconomic impact. The system has three main features: (1) Sensing data gathered from the accelerometer and Electrocardiogram (ECG) electrodes embedded in the Shimmer sensing device; (2) Real time monitoring and alerting system; and (3) Medical logging consists of time, position, strength, and location of the fall. The real-time classification of the fall detection has been achieved with an accuracy of 90% using K-Nearest Neighbors (KNN) algorithm. Moreover, the KNN hardware implementation requires 48% of Look-Up Tables (LUTs) and 22% of Flip-Flops (FFs) available on the Zedboard while consuming 582 Mw. Springer International Publishing AG 2018.
    DOI/handle
    http://dx.doi.org/10.1007/978-3-319-56991-8_62
    http://hdl.handle.net/10576/12791
    Collections
    • Computer Science & Engineering [‎2428‎ items ]
    • Electrical Engineering [‎2821‎ items ]

    entitlement


    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of Digital Hub
      Communities & Collections Publication Date Author Title Subject Type Language Publisher
    This Collection
      Publication Date Author Title Subject Type Language Publisher

    My Account

    Login

    Statistics

    View Usage Statistics

    About QSpace

    Vision & Mission

    Help

    Item Submission Publisher policiesUser guides FAQs

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video