Automatic Recognition of Daily Living Activities Based on a Hierarchical Classifier

  • Oresti Banos
  • Miguel Damas
  • Hector Pomares
  • Ignacio Rojas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6692)

Abstract

Physical activity recognition has become an increasing research area specially on health-related fields. The amount of different postures, movements and exercises in addition to the difficulty of the individuals particular execution style determine that extremely robust efficient knowledge inference systems are extremely necessary, being classification process one of the most crucial steps. Considering the power of binary classification in contrast to direct multiclass approaches, and the capabilities offered by multi-sense environments, we define a novel classification schema based on hierarchical structures composed by weighted decision makers. Remarkable accuracy results are obtained for a particular activity recognition problem in contrast to a traditional multiclass majority voting algorithm.

Keywords

hierarchical classification weighted classification binary classifiers activity recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1, 113–141 (2001)MathSciNetMATHGoogle Scholar
  2. 2.
    Baca, A., Dabnichki, P., Heller, M., Kornfeind, P.: Ubiquitous computing in sports: A review and analysis. Journal of Sports Sciences 27, 1335–1346 (2009)CrossRefGoogle Scholar
  3. 3.
    Banos, O., Pomares, H., Rojas, I.: Ambient living activity recognition based on feature-set ranking using intelligent systems. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–4 (2010)Google Scholar
  4. 4.
    Bao, L., Intille, S.: Activity Recognition from User-Annotated Acceleration Data. Pervasive Computing, 1–17 (2004)Google Scholar
  5. 5.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2010), http://archive.ics.uci.edu/ml
  6. 6.
    Koskimaki, H., Huikari, V., Siirtola, P., Laurinen, P., Roning, J.: Activity recognition using a wrist-worn inertial measurement unit: A case study for industrial assembly lines. In: 17th Mediterranean Conference on Control and Automation MED 2009, pp. 401–405 (2009)Google Scholar
  7. 7.
    Mathie, M.J., Coster, A.C.F., Lovell, N.H., Celler, B.G.: Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiol. Meas. 25, 1–20 (2004)CrossRefGoogle Scholar
  8. 8.
    Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J., Korhonen, I.: Activity classification using realistic data from wearable sensors. IEEE Transactions on Information Technology in Biomedicine 10, 119–128 (2006)CrossRefGoogle Scholar
  9. 9.
    Preece, S.J., Goulermas, J.Y., Kenney, L.P.J., Howard, D., Meijer, K., Crompton, R.: Activity identification using body-mounted sensors—a review of classification techniques. Physiol. Meas. 30, 1–33 (2009)CrossRefGoogle Scholar
  10. 10.
    Sazonov, E., Fulk, G., Sazonova, N., Schuckers, S.: Automatic Recognition of postures and activities in stroke patients. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC 2009, pp. 2200–2203 (2009)Google Scholar
  11. 11.
    Schlömer, T., Poppinga, B., Henze, N., Boll, S.: Gesture recognition with a Wii controller. In: Proceedings of the 2nd international conference on Tangible and embedded interaction - TEI 2008 (2008)Google Scholar
  12. 12.
    Singla, G., Cook, D., Schmitter-Edgecombe, M.: Incorporating temporal reasoning into activity recognition for smart home residents. In: AAAI Workshop - Technical Report WS-08-11, pp. 53–61 (2008)Google Scholar
  13. 13.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Elsevier, Amsterdam (2009)MATHGoogle Scholar
  14. 14.
    Warren, J.M., et al.: Assessment of physical activity – a review of methodologies with reference to epidemiological research: a report of the exercise physiology section of the European Association of Cardiovascular Prevention and Rehabilitation. European Journal of Cardiovascular Prevention & Rehabilitation 17, 127–139 (2010)CrossRefGoogle Scholar
  15. 15.
    Zwartjes, D., Heida, T., van Vugt, J., Geelen, J., Veltink, P.: Ambulatory Monitoring of Activities and Motor Symptoms in Parkinson’s Disease. IEEE Transactions on Biomedical Engineering 57, 2778–2786 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Oresti Banos
    • 1
  • Miguel Damas
    • 1
  • Hector Pomares
    • 1
  • Ignacio Rojas
    • 1
  1. 1.Department of Computer Architecture and Computer TechnologyUniversity of GranadaGranadaSpain

Personalised recommendations