• DocumentCode
    676715
  • Title

    Activities of daily living classification using depth features

  • Author

    Da Luz, Laurence ; Masek, Martin ; Chiou Peng Lam

  • Author_Institution
    Sch. of Comput. & Security Sci., Edith Cowan Univ., Perth, WA, Australia
  • fYear
    2013
  • fDate
    22-25 Oct. 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The increasing elderly population presents a challenge on the resources of carers and assisted living communities. In this paper, we present an algorithm based around the Microsoft Kinect for monitoring activities of daily living. The system analyses the behaviour of occupants to provide carers with valuable observational data, and has the capacity to detect abnormal events in the home.
  • Keywords
    assisted living; feature extraction; image classification; image sensors; Microsoft Kinect; abnormal events detection; activities-of-daily living classification; assisted living communities; carers; depth features; elderly population; observational data; occupants behavior; Aging; Cameras; Monitoring; Sensors; Streaming media; Training data; Vectors; assisted living; patient monitoring; smart homes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    TENCON 2013 - 2013 IEEE Region 10 Conference (31194)
  • Conference_Location
    Xi´an
  • ISSN
    2159-3442
  • Print_ISBN
    978-1-4799-2825-5
  • Type

    conf

  • DOI
    10.1109/TENCON.2013.6718892
  • Filename
    6718892
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=676715