Advertisement

Application of Statistical Methods to Improve an Acceleration Based Algorithm

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

Abstract

Falls are the leading reason for death related accidents in people over 65 years old. Concerning this situation, it is necessary to develop a viable way of detecting these falls as fast as possible, so that medical assistance can be provided within useful time.

In order for a system of this kind to work correctly, it must have a low percentage of false positives and a good autonomy. In this paper we present the research done in order to improve an existing acceleration based algorithm, which despite being inaccurate is however highly energy efficient. The study of its improvement was done resorting to the use of cluster analysis and logistic regression.

The resulting algorithm distinguishes itself by being, at the same time, very accurate and having low energy consumption.

Keywords

health monitoring logistic regression fall detection cluster analysis wireless sensor network aging 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Giannakouris, K.: Ageing characterises the demographic perspectives of the European societies. Eurostat: Statistics in Focus. Retrieved 9, 08–072 (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.
    Jehoel-Gijsbers, G., Vrooman, C.: Social exclusion of the elderly: A comparative study of EU Member States. Ceps (2008)Google Scholar
  4. 4.
    Johnson, R., Wichern, D.: Applied Multivariate Statistical Analysis, 3rd edn. Prentice Hall, New Jersey (1992)zbMATHGoogle Scholar
  5. 5.
    McCullagh, P., Nelder, J.: Generalized linear models. Chapman & Hall/CRC, Boca Raton (1989)CrossRefzbMATHGoogle Scholar
  6. 6.
    Lord, C., Colvin, D.: Falls in the elderly: Detection and assessment. In: Proceedings of the Annual International Conference of the IEEE, pp. 1938–1939 (October 1991)Google Scholar
  7. 7.
    Wu, G.: Distinguishing fall activities from normal activities by velocity characteristics. Journal of Biomechanics 33, 1497–1500 (2000)CrossRefGoogle Scholar
  8. 8.
    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. In: Engineering in Medicine and Biology Society, EMBS 2008, pp. 2832 –2835 (August 2008)Google Scholar
  9. 9.
    Pflimlin, J., Hamel, T., Soueres, P., Metni, N.: Nonlinear attitude and gyroscope’s bias estimation for a VTOL UAV. International Journal of Systems Science 38(3), 197–210 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    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
  11. 11.
    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
  12. 12.
    Imote2 datasheet. PDF, http://tinyurl.com/6bt9zqb
  13. 13.
    Its400 data sheet. PDF, http://tinyurl.com/5rqjqdq
  14. 14.
    Corporation, I.: Ibm spss statistics (April 2011), http://www.spss.com/
  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.
    Belsley, D., Kuh, E., Welsch, R.: Regression diagnostics: Identifying influential data and sources of collinearity. Wiley-Interscience, Hoboken (2004)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Filipe Felisberto
    • 1
  • Miguel Felgueiras
    • 1
    • 2
  • Alexandra Seco
    • 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