Novel Method for Feature-Set Ranking Applied to Physical Activity Recognition

  • Oresti Baños
  • Héctor Pomares
  • Ignacio Rojas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6097)


Considerable attention is recently being paid in e-health and e-monitoring to the recognition of motion, postures and physical exercises from signal activity analysis. Most works are based on knowledge extraction using features which permit to make decisions about the activity realized, being feature selection the most critical stage. Feature selection procedures based on wrapper methods or ‘branch and bound’ are highly computationally expensive. In this paper, we propose an alternative filter method using a feature-set ranking via a couple of two statistical criteria, which achieves remarkable accuracy rates in the classification process.


Activity Recognition Feature Selection Ranking 


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  1. 1.
    Baek, J., Lee, G., Park, W., Yun, B.J.: Accelerometer Signal Processing for User Activity Detection. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3215, pp. 1611–3349. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Bao, L., Intille, S.S.: Activity Recognition from User-Annotated Acceleration Data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Bonomi, A.G., Goris, A.H.C., Yin, B., Westerterp, K.R.: Detection of Type, Duration, and Intensity of Physical Activity Using an Accelerometer. Medicine & Science in Sports & Exercise 41, 1770–1777 (2009)CrossRefGoogle Scholar
  4. 4.
    Crampton, N., Fox, K., Johnston, H., Whitehead, A.: Dance, Dance Evolution: Accelerometer Sensor Networks as Input to Video Games. In: IEEE International Workshop on Haptic, Audio and Visual Environments and Games, pp. 107–112 (2007)Google Scholar
  5. 5.
    Ermes, M., Pärkka, J., Mantyjarvi, J., Korhonen, I.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans. Inf. Technol. Biomed. 12, 20–26 (2008)CrossRefGoogle Scholar
  6. 6.
    Kohavi, R., Sommereld, D.: Feature Subset Selection Using the Wrapper Method: Overtting and Dynamic Search Space Topology. In: First International Conference on Knowledge Discovery and Data Mining (1995)Google Scholar
  7. 7.
    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, pp. 401–405 (2009)Google Scholar
  8. 8.
    Lee, S.W., Mase, K.: Activity and location recognition using wearable sensors. IEEE Pervasive Computing 1, 24–32 (2002)CrossRefGoogle Scholar
  9. 9.
    Mantyjarvi, J., Himberg, J., Seppanen, T.: Recognizing human motion with multiple acceleration sensors. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 747–752 (2001)Google Scholar
  10. 10.
    McCue1, M., Hodgins, J., Bargteil, A.: Telerehabilitation in Employment/Community Supports Using Videobased. In: Activity Recognition. RERC on Telereahabilitation (2008)Google Scholar
  11. 11.
    Munguia, E., Intille, S.S., Haskell, W., Larson, K., Wright, J., King, A., Friedman, R.: Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor. In: Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers, pp. 1–4 (2007)Google Scholar
  12. 12.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity Recognition from Accelerometer Data. In: Proceedings of the 17th conference on Innovative applications of artificial intelligence, pp. 1541–1546 (2005)Google Scholar
  13. 13.
    Song, L., Smola, A., Gretton, A., Borgwardt, K.M., Bedo, J.: Supervised feature selection via dependence estimation. In: Proceedings of the 24th international conference on Machine learning, pp. 823–830 (2007)Google Scholar
  14. 14.
    Xu, Z., Jin, R., Ye, J., Lyu, M.R., King, I.: Non-monotonic feature selection. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1145–1152 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Oresti Baños
    • 1
  • Héctor Pomares
    • 1
  • Ignacio Rojas
    • 1
  1. 1.Department of Computer Architecture and Computer TechnologyUniversity of GranadaGranadaSpain

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