Advertisement

Improving the Elder Care’s Wireless Sensor Network Fall Detection System Using Logistic Regression

  • Filipe Felisberto
  • Miguel Felgueiras
  • Patricio Domingues
  • Florentino Fdez-Riverola
  • António Pereira
Part of the Communications in Computer and Information Science book series (CCIS, volume 221)

Abstract

The world’s population is aging; we are already facing many socioeconomic challenges directly related to this problem. These challenges will only tend to grow as time passes. If viable solutions are not found in time, these challenges will become unbearable as the elderly population surpasses the younger population.

One of the more serious health problems faced by the elderly are falls that are not succored fast enough. In this paper we discuss the motivations behind our work and specially our focus on fall detection.

We will also present the new Elder Care’s fall detection system, resultant of our research in the area of statistical regression.

Keywords

health monitoring logistic regression fall detection wireless sensor network body area network aging 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Marcelino, I.: Remote monitoring and social isolation prevention structured system for elders. Master’s thesis, University of Trás-os-Montes e Alto Douro (2009)Google Scholar
  2. 2.
    Marcelino, I., Barroso, J., Bulas Cruz, J., Pereira, A.: Elder care architecture. In: Proceedings of the 2008 Third International Conference on Systems and Networks Communications, pp. 349–354 (2008)Google Scholar
  3. 3.
    Moreira, N., Felisberto, F., Marcelino, I., Pereira, A.: A scalable architecture for body area networks. Submitted to the European Association for Signal Processing (2010)Google Scholar
  4. 4.
    Kinsella, K., He, W., Bureau, U.C.: An aging world: 2008: International population reports. US Government Printing Office (2009)Google Scholar
  5. 5.
    Giannakouris, K.: Ageing characterises the demographic perspectives of the European societies. Eurostat: Statistics in Focus 9, 08–072 (2009)Google Scholar
  6. 6.
    Carone, G.: de las Comunidades Europeas. Dirección General de Asuntos Económicos y Financieros, C.: Long-term labour force projections for the 25 EU Member States: A set of data for assessing the economic impact of ageing. European Commission, Directorate-General for Economic and Financial Affairs (2005)Google Scholar
  7. 7.
    Marcelino, I., Barroso, J., Bulas Cruz, J., Pereira, A.: Elder care architecture, a physical and social approach. International Journal on Advances in Life Sciences 2(1&2), 53–62 (2010)Google Scholar
  8. 8.
    Todd, C., Skelton, D.: What are the main risk factors for falls among older people and what are the most effective interventions to prevent these falls? Technical report, WHO Regional Office for Europe (Health Evidence Network report) (2004)Google Scholar
  9. 9.
    Correll, J., McNaughton, J.: Igloo White. Air Force Magazine 87(11) (2004)Google Scholar
  10. 10.
    Yang, G.Z.: Body sensor networks. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Karulf, E.: Body area networks (ban). pdf (April 2008) http://www.cse.wustl.edu/~jain/cse574-08/ftp/ban.pdf
  12. 12.
    IEEE: Ieee 802.15 wpan task group 6 (tg6) body area networks (June 2011), http://www.ieee802.org/15/pub/TG6.html
  13. 13.
    Nelder, J., Wedderburn, R.: Generalized linear models. Journal of the Royal Statistical Society. Series A (General) 135(3), 370–384 (1972)CrossRefGoogle Scholar
  14. 14.
    McCullagh, P., Nelder, J.: Generalized linear models. Chapman & Hall/CRC, Boca Raton (1989)CrossRefzbMATHGoogle Scholar
  15. 15.
    Hosmer, D., Lemeshow, S.: Applied logistic regression. Wiley Interscience, Hoboken (2000)CrossRefzbMATHGoogle Scholar
  16. 16.
    Seco, A., Felgueiras, M., Fdez-Riverola, F., Pereira, A.: Elder Care Alert Management-Decision Support by a Logistic Regression Model. In: Corchado, J.M., Pérez, J.B., Hallenborg, K., Golinska, P., Corchuelo, R. (eds.) Trends in Practical Applications of Agents and Multiagent Systems. AISC, vol. 90, pp. 9–16. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Felisberto, F., Moreira, N., Marcelino, I., Fdez-Riverola, F., Pereira, A.: Elder care’s fall detection system. Ambient Intelligence-Software and Applications, 85–92 (April 2011)Google Scholar
  18. 18.
    Bourke, A.K., O’Donovan, K.J., Nelson, J., OLaighin, G.M.: Fall-detection through vertical velocity thresholding using a tri-axial accelerometer characterized using an optical motion-capture system, pp. 2832–2835 (August 2008)Google Scholar
  19. 19.
    Wu, G.: Distinguishing fall activities from normal activities by velocity characteristics. Journal of Biomechanics 33, 1497–1500 (2000)CrossRefGoogle Scholar
  20. 20.
    Mathie, M., Lovell, N., Coster, A., Celler, B.: Determining activity using a triaxial accelerometer. In: Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, Engineering in Medicine and Biology, 2002, vol. 3, pp. 2481–2482. IEEE, Los Alamitos (2002)CrossRefGoogle Scholar
  21. 21.
    van de Ven, P., Bourke, A.K., Nelson, J., Laighin, G.O.: A wireless platform for fall and mobility monitoring. In: Signals and Systems Conference (2008)Google Scholar
  22. 22.
  23. 23.
  24. 24.
    Corporation, I.: Ibm spss statistics (April 2011), http://www.spss.com

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Filipe Felisberto
    • 1
  • Miguel Felgueiras
    • 1
    • 2
  • Patricio Domingues
    • 1
  • Florentino Fdez-Riverola
    • 3
  • António Pereira
    • 1
    • 4
  1. 1.School of Technology and Management, Computer Science and Communications Research CentrePolytechnic Institute of LeiriaLeiriaPortugal
  2. 2.CEAULLisbonPortugal
  3. 3.ESEI: Escuela Superior de Ingeniería InformáticaUniversity of Vigo, Edificio PolitécnicoOurenseSpain
  4. 4.INOV INESC INOVAÇÃO – Instituto de Novas TecnologiasLeiriaPortugal

Personalised recommendations