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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Allen, D.: Getting Things Done: The Art of Stress-Free Productivity. Viking, New York (2001)
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)
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)
Friedman, J., Hastie, T., Tibshirani, R.: Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics 28 (1998)
Huynh, T., Schiele, B.: Analyzing Features for Activity Recognition. In: Proceedings of Smart Objects & Ambient Intelligence Conference (2005)
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)
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)
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)
Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. In: Proceedings of IAAI 2005 (2005)
Schapire, R.: The Boosting Approach to Machine Learning: An Overview. In: MSRI Workshop on Nonlinear Estimation and Classification (2003)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Prekopcsák, Z., Soha, S., Henk, T., Gáspár-Papanek, C. (2009). Activity Recognition for Personal Time Management. In: Tscheligi, M., et al. Ambient Intelligence. AmI 2009. Lecture Notes in Computer Science, vol 5859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05408-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-05408-2_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05407-5
Online ISBN: 978-3-642-05408-2
eBook Packages: Computer ScienceComputer Science (R0)