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Activity Recognition for Personal Time Management

  • Zoltán Prekopcsák
  • Sugárka Soha
  • Tamás Henk
  • Csaba Gáspár-Papanek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5859)

Abstract

We describe an accelerometer based activity recognition system for mobile phones with a special focus on personal time management. We compare several data mining algorithms for the automatic recognition task in the case of single user and multiuser scenario, and improve accuracy with heuristics and advanced data mining methods. The results show that daily activities can be recognized with high accuracy and the integration with the RescueTime software can give good insights for personal time management.

Keywords

Support Vector Machine Mobile Phone Activity Recognition Data Mining Algorithm Smart Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Allen, D.: Getting Things Done: The Art of Stress-Free Productivity. Viking, New York (2001)Google 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)Google Scholar
  3. 3.
    Choudhury, T., Borriello, G., et al.: The Mobile Sensing Platform: An Embedded System for Activity Recognition. In: IEEE Pervasive Magazine - Special Issue on Activity-Based Computing (2008)Google Scholar
  4. 4.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics 28 (1998)Google Scholar
  5. 5.
    Huynh, T., Schiele, B.: Analyzing Features for Activity Recognition. In: Proceedings of Smart Objects & Ambient Intelligence Conference (2005)Google Scholar
  6. 6.
    Kern, N., Schiele, B., Schmidt, A.: Multi-Sensor Activity Context Detection for Wearable Computing. In: Aarts, E., Collier, R.W., van Loenen, E., de Ruyter, B. (eds.) EUSAI 2003. LNCS, vol. 2875, pp. 220–232. Springer, Heidelberg (2003)Google Scholar
  7. 7.
    Mathie, M.J., Celler, B.G., Lovell, N.H., Coster, A.C.: Classification of basic daily movements using a triaxial accelerometer. Medical & Biological Engineering & Computing 42, 679–687 (2004)CrossRefGoogle Scholar
  8. 8.
    Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006 (2006)Google Scholar
  9. 9.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. In: Proceedings of IAAI 2005 (2005)Google Scholar
  10. 10.
    Schapire, R.: The Boosting Approach to Machine Learning: An Overview. In: MSRI Workshop on Nonlinear Estimation and Classification (2003)Google Scholar
  11. 11.
    Uiterwaal, M., Glerum, E.B.C., Busser, H.J., Van Lummel, R.C.: Ambulatory monitoring of physical activity in working situations, a validation study. Journal of Medical Engineering & Technology 22 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zoltán Prekopcsák
    • 1
  • Sugárka Soha
    • 1
  • Tamás Henk
    • 1
  • Csaba Gáspár-Papanek
    • 1
  1. 1.Budapest University of Technology and EconomicsHungary

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