IDEAS home Printed from https://ideas.repec.org/a/hin/jnljam/896030.html
   My bibliography  Save this article

Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model

Author

Listed:
  • Dongha Lim
  • Chulho Park
  • Nam Ho Kim
  • Sang-Hoon Kim
  • Yun Seop Yu

Abstract

Falls are a serious medical and social problem among the elderly. This has led to the development of automatic fall-detection systems. To detect falls, a fall-detection algorithm that combines a simple threshold method and hidden Markov model (HMM) using 3-axis acceleration is proposed. To apply the proposed fall-detection algorithm and detect falls, a wearable fall-detection device has been designed and produced. Several fall-feature parameters of 3-axis acceleration are introduced and applied to a simple threshold method. Possible falls are chosen through the simple threshold and are applied to two types of HMM to distinguish between a fall and an activity of daily living (ADL). The results using the simple threshold, HMM, and combination of the simple method and HMM were compared and analyzed. The combination of the simple threshold method and HMM reduced the complexity of the hardware and the proposed algorithm exhibited higher accuracy than that of the simple threshold method.

Suggested Citation

  • Dongha Lim & Chulho Park & Nam Ho Kim & Sang-Hoon Kim & Yun Seop Yu, 2014. "Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-8, September.
    • Handle: RePEc:hin:jnljam:896030
    DOI: 10.1155/2014/896030
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/JAM/2014/896030.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/JAM/2014/896030.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2014/896030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sadik Kamel Gharghan & Saleem Latteef Mohammed & Ali Al-Naji & Mahmood Jawad Abu-AlShaeer & Haider Mahmood Jawad & Aqeel Mahmood Jawad & Javaan Chahl, 2018. "Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network," Energies, MDPI, vol. 11(11), pages 1-32, October.

    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:hin:jnljam:896030. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.