Advertisement

Detecting Domestic Problems of Elderly People: Simple and Unobstrusive Sensors to Generate the Context of the Attended

  • Juan A. Botia
  • Ana Villa
  • Jose T. Palma
  • David Pérez
  • Emilio Iborra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5518)

Abstract

Unexpected falls and/or heart attacks at home are one of the main accidents the elderly face nowadays. This work focuses on elderly people which yet are independent and live alone in their own house. In such cases, the mentioned accidents may prevent her to ask for help as it is possible that she may lose conscience or stay paralyzed at the floor. In this paper, it is shown how a rule based classifier, designed by using simple a priori knowledge, which incorporates elderly’s context information and simple adaptive mechanisms for this information, may be used to detect domestic accidents as quickly as possible.

Keywords

Elderly People Heart Attack Pressure Sensor Activity Sensor Rule Base Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alwan, M., Dalal, S., Seifrafi, R., Kell, S., Brown, D.: A rule-based approach to the analysis of elders activity data. Technical report, Medical Automation Research Center (2007)Google Scholar
  2. 2.
    Jansen, B., Deklerck, R.: Context aware inactivity recognition for visual fall detection. In: Pervasive Health Conference and Workshops (2006)Google Scholar
  3. 3.
    LeBellego, N., Noury, G.V., Mousseau, M., Demongeot, J.: A model for the measurement of patient activity in a hospital suite. IEEE Transactions on Information Technology in Biomedicine 10(1), 92–99 (2006)CrossRefGoogle Scholar
  4. 4.
    Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Learning and inferring transportation routines. Artificial Intelligence 5(6), 311–331 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  6. 6.
    Quinlan, J.R.: C4.5: Programs For Machine Learning. The Morgan Kaufmann series in Machine Learning. Morgan-Kauffman, San Mateo (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Juan A. Botia
    • 1
  • Ana Villa
    • 1
  • Jose T. Palma
    • 1
  • David Pérez
    • 1
  • Emilio Iborra
    • 1
  1. 1.Universidad de MurciaSpain

Personalised recommendations