Abstract
We are developing a personal activity recognition system that is practical, reliable, and can be incorporated into a variety of health-care related applications ranging from personal fitness to elder care. To make our system appealing and useful, we require it to have the following properties: (i) data only from a single body location needed, and it is not required to be from the same point for every user; (ii) should work out of the box across individuals, with personalization only enhancing its recognition abilities; and (iii) should be effective even with a cost-sensitive subset of the sensors and data features. In this paper, we present an approach to building a system that exhibits these properties and provide evidence based on data for 8 different activities collected from 12 different subjects. Our results indicate that the system has an accuracy rate of approximately 90% while meeting our requirements. We are now developing a fully embedded version of our system based on a cell-phone platform augmented with a Bluetooth-connected sensor board.
Keywords
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Morris, M., Lundell, J., Dishman, E., Needham, B.: New perspectives on ubiquitous computing from ethnographic study of elders with cognitive decline. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 227–242. Springer, Heidelberg (2003)
Lawton, M.P.: Aging and Performance of Home Tasks. Human Factors (1990)
Consolvo, S., Roessler, P., Shelton, B., LaMarcha, A., Schilit, B., Bly, S.: Technology for Care Networks of Elders. In: Proc. IEEE Pervasive Computing Mobile and Ubiquitous Systems: Successful Aging (2004)
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)
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)
Lukowicz, P., Junker, H., Stäger, M., von Büren, T., Tröster, G.: WearNET: A Distributed Multi-sensor System for Context Aware Wearables. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, pp. 361–370. Springer, Heidelberg (2002)
Choudhury, T., Lester, J., Kern, N., Borriello, G., Hannaford, B.: A Hybrid Discriminative/Generative Approach for Modeling Human Activities. In: 19th International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh, Scotland (2005)
Park, S., Locher, I., Savvides, A., Srivastava, M., Chen, A., Muntz, R., Yuen, S.: Design of a Wearable Sensor Badge for Smart Kindergarten. In: Proc. 6th International Symposium on Wearable Computers, pp. 231–238 (2002)
Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., Anderson, J.: Wireless Sensor Networks for Habitat Monitoring. In: Proc. Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, pp. 88–97 (2002)
Smailagic, A., Currens, B., Maurer, U., Rowe, A.: eWatch. [Online], available http://flat-earth.ece.cmu.edu/~eWatch/
Rubinstein, Y.D., Hastie, T.: Discriminative vs. informative learning. In: Proceedings of Knowledge Discovery and Data Mining, pp. 49–53 (1997)
Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: Proc. Computer Vision and Pattern Recognition (2001)
Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S.: Boosting the margin: a new explanation for the effectiveness of voting methods. In: Proc., pp. 322–330 (1997)
Oliver, N., Horvitz, E.: Selective Perception Policies for Limiting Computation in Multimodal Systems: A Comparative Analysis. In: Proc. Proceedings of Int. Conf. on Multimodal Interfaces (2003)
Jaakkola, T., Haussler: Exploiting generative models in discriminative classifiers. In: Proc. in Advances in Neural Information Processing Systems (1999)
Zhang, F., Pi-Sunyer, F.X., Boozer, C.N.: Improving Energy Expenditure Estimation for Physical Activity. In: Medicine and Science in Sports and Exercise, pp. 883–889 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lester, J., Choudhury, T., Borriello, G. (2006). A Practical Approach to Recognizing Physical Activities. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds) Pervasive Computing. Pervasive 2006. Lecture Notes in Computer Science, vol 3968. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11748625_1
Download citation
DOI: https://doi.org/10.1007/11748625_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33894-9
Online ISBN: 978-3-540-33895-6
eBook Packages: Computer ScienceComputer Science (R0)