• DocumentCode
    636362
  • Title

    Artificial Neural Networks as an alternative to traditional fall detection methods

  • Author

    Vallejo, Monica ; Isaza, Claudia V. ; Lopez, Jose D.

  • Author_Institution
    Dept. of Electron. Eng., Univ. de Antioquia, Medellin, Colombia
  • fYear
    2013
  • fDate
    3-7 July 2013
  • Firstpage
    1648
  • Lastpage
    1651
  • Abstract
    Falls are common events among older adults and may have serious consequences. Automatic fall detection systems are becoming a popular tool to rapidly detect such events, helping family or health personal to rapidly help the person that falls. This paper presents the results obtained in the process of testing a new fall detection method, based on Artificial Neural Networks (ANN). This method intends to improve fall detection accuracy, by avoiding the traditional threshold - based fall detection methods, and introducing ANN as a suitable option on this application.Also ANN have low computational cost, this characteristic makes them easy to implement on a portable device, comfortable to be wear by the patient.
  • Keywords
    biomedical equipment; geriatrics; medical signal detection; medical signal processing; neural nets; portable instruments; ANN; artificial neural networks; automatic fall detection systems; fall detection accuracy; older adults; portable device; traditional fall detection methods; Acceleration; Accelerometers; Artificial neural networks; Biomedical monitoring; Neurons; Sensors; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
  • Conference_Location
    Osaka
  • ISSN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/EMBC.2013.6609833
  • Filename
    6609833