Advertisement

Activity Recognition Using Dynamic Instance Activation

  • Alberto Calzada
  • Chris NugentEmail author
  • Macarena Espinilla
  • Jonathan Synnott
  • Luis Martinez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10070)

Abstract

Dynamic Instance Activation (DIA) is a newly developed data-driven classification algorithm. It was designed to minimise the negative impact in situations of data incompleteness and inconsistency. To achieve this, the proposed methodology attempts to maximise the accuracy of the classification process in a way that does not compromise the overall computational effort.

In this research, DIA was evaluated in the context of Human Activity Recognition (HAR) for Smart Environments, using datasets consisting of binary sensor data and their associated class labels (activities). This scenario was selected as an ideal case study to illustrate the usefulness of DIA considering the wide range of domains in which HAR is applied. It was also considered adequate given the simplicity of the data involved in the process, which allows using relatively simple similarity functions, therefore placing the main focus on DIA’s performance.

In this context, DIA was compared with other state-of-the-art classifiers, delivering promising results in terms of percentage of activities correctly identified over the total. It is important to note that these results could be further improved if other similarity functions or data representation schemes were selected.

Keywords

Activity recognition Classification Smart environments Similarity measures 

Notes

Acknowledgements

Invest Northern Ireland partially supported this project under the Competence Centre Program Grant RD0513853 – Connected Health Innovation Centre.

References

  1. 1.
    Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.W.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C 42(6), 790–808 (2012)CrossRefGoogle Scholar
  2. 2.
    Calzada, A., Nugent, C., Espinilla, M., Martinez, L.: Generalized dynamic instance activation for activity recognition. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Orlando, Florida, US, 17–20 August 2016Google Scholar
  3. 3.
    Calzada, A., Liu, J., Wang, H., Kashyap, A.: A new dynamic rule activation method for extended belief rule-bases. IEEE Trans. Knowl. Data Eng. 27(4), 880–894 (2014)CrossRefGoogle Scholar
  4. 4.
    Liu, J., Martinez, L., Calzada, A., Wang, H.: A novel belief rule base representation, generation and its inference methodology. Knowl. Based Syst. 53, 129–141 (2013)CrossRefGoogle Scholar
  5. 5.
    Hamming, R.W.: Error detecting and error correcting codes. Bell Syst. Tech. J. 29(2), 147–160 (1950)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Norouzi, M., Fleet, D.J., Salakhutdinov R.R.: Hamming distance metric learning. In: Advances in Neural Information Processing Systems, pp. 1061–1069 (2012)Google Scholar
  7. 7.
    van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: Transferring knowledge of activity recognition across sensor networks. In: Floréen, P., Krüger, A., Spasojevic, M. (eds.) Pervasive 2010. LNCS, vol. 6030, pp. 283–300. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Cook, D., Schmitter-Edgecombe, M.: Assessing the quality of activities in a smart environment. Methods Inf. Med. 48(5), 480–485 (2009)CrossRefGoogle Scholar
  9. 9.
    Nugent, C., Synnott, J., Santanna, A., Espinilla, M., Cleland, I., Banos, O., Lundström, J., Hallberg, J., Calzada, A.: An initiative for the creation of open datasets within the pervasive healthcare. In: 10th EAI International Conference on Pervasive Computing Technologies for Healthcare, Cancun, Mexico, 16–19 May 2016Google Scholar
  10. 10.
    Synnott, J., Chen, L., Nugent, C.D., Moore, G.: The creation of simulated activity datasets using a graphical intelligent environment simulation tool. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Chicago, IL, USA, 26–30 August 2014Google Scholar
  11. 11.
    Domingos, P., Pazzani, M.: On the optimality of the simple bayesian classifier under zero-one loss. Mach. Learn. 29, 103–137 (1997)CrossRefzbMATHGoogle Scholar
  12. 12.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefzbMATHGoogle Scholar
  13. 13.
    Kohavi, R.: The power of decision tables. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 174–189. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  14. 14.
    Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20, 273–297 (1995)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Alberto Calzada
    • 1
  • Chris Nugent
    • 1
    Email author
  • Macarena Espinilla
    • 2
  • Jonathan Synnott
    • 1
  • Luis Martinez
    • 2
  1. 1.School of Computing and MathematicsUniversity of UlsterJordanstownNorthern Ireland, UK
  2. 2.Department of Computer SciencesUniversity of JaenJaenSpain

Personalised recommendations