IDEAS home Printed from https://ideas.repec.org/a/igg/jhisi0/v19y2024i1p1-12.html
   My bibliography  Save this article

Application of Behavior Recognition Technology Based on Deep Learning in Elderly Care

Author

Listed:
  • Shihui Zhang

    (HeBei North University, China)

  • Jing Mi

    (HeBei North University, China)

  • Naidi Liu

    (HeBei North University, China)

Abstract

China is currently one of the countries with the largest elderly population in the world, and the issue of population aging has become a widespread concern. The behavior recognition algorithm based on deep learning is currently the main behavior recognition algorithm and one of the basic technologies in the field of computer vision. In existing research, the method of constructing complex classification models based on manual feature representation can no longer meet the requirements of high recognition accuracy and applicability, and the introduction of deep learning has brought new development directions for behavior recognition. Therefore, this article aims to study how to apply deep learning-based behavior recognition technology more accurately and effectively in the care of elderly people in the context of “artificial intelligence.”

Suggested Citation

  • Shihui Zhang & Jing Mi & Naidi Liu, 2024. "Application of Behavior Recognition Technology Based on Deep Learning in Elderly Care," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 19(1), pages 1-12, January.
    • Handle: RePEc:igg:jhisi0:v:19:y:2024:i:1:p:1-12
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJHISI.336548
    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:jhisi0:v:19:y:2024:i:1: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.

    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.