Feature Sub-set Selection for Activity Recognition

  • Francisco J. Quesada
  • Francisco Moya
  • Macarena Espinilla
  • Luis Martínez
  • Chris D. Nugent
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9102)

Abstract

The delivery of Ambient Assisted Living services, specifically relating to the smart-home paradigm, assumes that people can be provided with help, automatically and in real time, in their homes as and when required. Nevertheless, the deployment of a smart-home can lead to high levels of expense due to configuration requirements of multiple sensing and actuating technology. In addition, the vast amount of data produced leads to increased levels of computational complexity when trying to ascertain the underlying behavior of the inhabitant. This contribution presents a methodology based on feature selection which aims to reduce the number of sensors required whilst still maintaining acceptable levels of activity recognition performance. To do so, a smart-home dataset has been utilized, obtaining a configuration of sensors with the half sensors with respect to the original configuration.

Keywords

Activity recognition Smart-homes Feature selection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Streitz, N., Nixon, P.: The disappearing computer. Communications of the ACM 48(3), 32–35 (2005)CrossRefGoogle Scholar
  2. 2.
    John, G.H., Kohavi, R., Pfleger, K., et al.: Irrelevant features and the subset selection problem. ICML 94, 121–129 (1994)Google Scholar
  3. 3.
    Koller, D., Sahami, M.: Toward optimal feature selection. Stanford InfoLab (1996)Google Scholar
  4. 4.
    Dash, M., Liu, H.: Feature selection for classification. Intelligent data analysis 1(3), 131–156 (1997)CrossRefGoogle Scholar
  5. 5.
    Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(6), 790–808 (2012)CrossRefGoogle Scholar
  6. 6.
    Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Philip, S.Y., et al.: Top 10 algorithms in data mining. Knowledge and Information Systems 14(1), 1–37 (2008)CrossRefGoogle Scholar
  7. 7.
    van Kasteren, T., Krose, B.: Bayesian activity recognition in residence for elders. IET (2007)Google Scholar
  8. 8.
    Vapnik, V.: The nature of statistical learning theory. Springer Science & Business Media (2000)Google Scholar
  9. 9.
    Cherkassky, V., Mulier, F.M.: Learning from data: concepts, theory, and methods. John Wiley & Sons (2007)Google Scholar
  10. 10.
    Van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 1–9. ACM (2008)Google Scholar
  11. 11.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD explorations newsletter 11(1), 10–18 (2009)CrossRefGoogle Scholar
  12. 12.
    Hall, M., Witten, I., Frank, E.: Data mining: Practical machine learning tools and techniques. Kaufmann, Burlington (2011)Google Scholar
  13. 13.
    Calzada, A., Liu, J., Wang, H., Kashyap, A.: Dynamic rule activation for extended belief rule bases. In: International Conference on Machine Learning and Cybernetics (ICMLC), vol. 4, pp. 1836–1841. IEEE (2013)Google Scholar
  14. 14.
    Kohavi, R., Sommerfield, D.: Feature subset selection using the wrapper method: Overfitting and dynamic search space topology. In: KDD, pp. 192–197 (1995)Google Scholar
  15. 15.
    Calzada, A., Liu, J., Nugent, C., Wang, H., Martinez, L.: Sensor-based activity recognition using extended belief rule-based inference methodology. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2694–2697. IEEE (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Francisco J. Quesada
    • 1
  • Francisco Moya
    • 1
  • Macarena Espinilla
    • 1
  • Luis Martínez
    • 1
  • Chris D. Nugent
    • 2
  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain
  2. 2.School of Computing and MathematicsUniversity of UlsterColeraineUK

Personalised recommendations