Reducing the Response Time for Activity Recognition Through use of Prototype Generation Algorithms

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

Abstract

The nearest neighbor approach is one of the most successfully deployed techniques used for sensor-based activity recognition. Nevertheless, this approach presents some disadvantages in relation to response time, noise sensitivity and high storage requirements. The response time and storage requirements are closely related to the data size. This notion of data size is an important issue in sensor-based activity recognition given the vast amounts of data produced within smart environments. A wide range of prototype generation algorithms, which are designed for use with the nearest neighbor approach, have been proposed in the literature to reduce the size of the data set. These algorithms build new artificial prototypes, which represent the data, and subsequently lead to an increase in the accuracy of the nearest neighbor approach. In this work, we discuss the use of prototype generation algorithms and their effect on sensor-based activity recognition using the nearest neighbor approach to classify activities, reducing the response time. A range of prototype generation algorithms based on positioning adjustment, which reduce the data size, are evaluated in terms of accuracy and reduction. These approaches have been compared with the normal nearest neighbor approach, achieving similar accuracy and reducing the data size. Analysis of the results attained provide the basis for the use of prototype generation algorithms for sensor-based activity recognition to reduce the overall response time of the nearest neighbor approach.

Keywords

Activity recognition Data-driven Nearest Neighbor (NN) Prototype Generation (PG) Response time 

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References

  1. 1.
    Satyanarayanan, M.: Pervasive computing: Vision and challenges. IEEE Personal Communications 8(4), 10–17 (2001)CrossRefGoogle Scholar
  2. 2.
    Emmanouilidis, C., Koutsiamanis, R.-A., Tasidou, A.: Mobile guides: Taxonomy of architectures, context awareness, technologies and applications. Journal of Network and Computer Applications 36(1), 103–125 (2013)CrossRefGoogle Scholar
  3. 3.
    Alam, M., Hamida, E.: Surveying wearable human assistive technology for life and safety critical applications: Standards, challenges and opportunities. Sensors (Switzerland) 14(5), 9153–9209 (2014)CrossRefGoogle Scholar
  4. 4.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21–27 (1967)CrossRefMATHGoogle Scholar
  5. 5.
    Wu, X., Kumar, V.: The Top Ten Algorithms in Data Mining. Chapman & Hall/CRC, 1st ed. (2009)Google Scholar
  6. 6.
    Moayeri Pour, G., Troped, P., Evans, J.: Environment feature extraction and classification for context aware physical activity monitoring, pp. 123–128 (2013)Google Scholar
  7. 7.
    Kononenko, I., Kukar, M.: Machine Learning and Data Mining: Introduction to Principles and Algorithms. Horwood Publishing Limited (2007)Google Scholar
  8. 8.
    Garcia, S., Derrac, J., Cano, J., Herrera, F.: Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(3), 417–435 (2012)CrossRefGoogle Scholar
  9. 9.
    Lozano, M., Sotoca, J., Sanchez, J., Pla, F., Pekalska, E., Duin, R.: Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces. Pattern Recognition 39(10), 1827–1838 (2006)CrossRefMATHGoogle Scholar
  10. 10.
    Wilson, R.D., Martinez, T.: Reduction techniques for instance-based learning algorithms. Machine Learning 38(3), 257–286 (2000)CrossRefMATHGoogle Scholar
  11. 11.
    Van Kasteren, T., Noulas, A., Englebienne, G., Krse, B.: Accurate activity recognition in a home setting, pp. 1–9 (2008)Google Scholar
  12. 12.
    Alcala Fdez, J., Fernandez, A., Luengo, J., Derrac, J., Garcia, S., Sanchez, L., Herrera, F.: Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. Journal of Multiple-Valued Logic and Soft Computing 17(2–3), 255–287 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

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

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