Abstract
Body Sensor Networks offer many applications in healthcare, well-being and entertainment. One of the emerging applications is recognizing activities of daily living. In this paper, we introduce a novel knowledge pattern named Emerging Sequential Pattern (ESP)—a sequential pattern that discovers significant class differences—to recognize both simple (i.e., sequential) and complex (i.e., interleaved and concurrent) activities. Based on ESPs, we build our complex activity models directly upon the sequential model to recognize both activity types. We conduct comprehensive empirical studies to evaluate and compare our solution with the state-of-the-art solutions. The results demonstrate that our approach achieves an overall accuracy of 91.89%, outperforming the existing solutions.
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References
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)
Logan, B., Healey, J., Philipose, M., Tapia, E.M., Intille, S.: A long-term evaluation of sensing modalities for activity recognition. In: Krumm, J., Abowd, G.D., Seneviratne, A., Strang, T. (eds.) UbiComp 2007. LNCS, vol. 4717, pp. 483–500. Springer, Heidelberg (2007)
Patterson, D., Fox, D., Kautz, H., Philipose, M.: Fine-grained activity recognition by aggregating abstract object usage. In: Proc. IEEE Int’l Symp. Wearable Computers, Osaka (October 2005)
Vail, D.L., Veloso, M.M., Lafferty, J.D.: Conditional random fields for activity recognition. In: Proc. Int’l Conf. Autonomous Agents and Multi-agent Systems, AAMAS (2007)
van Kasteren, T.L.M., Noulas, A.K., Englebienne, G., Kröse, B.J.A.: Accurate activity recognition in a home setting. In: Proc. Int’l Conf. Ubicomp, Seoul, Korea (September 2008)
Modayil, J., Bai, T.X., Kautz, H.: Improving the recognition of interleaved activities. In: Proc. Int’l Conf. Ubicomp, Seoul, South Korea (September 2008)
Wu, T.Y., Lian, C.C., Hsu, J.Y.: Joint recognition of multiple concurrent activities using factorial conditional random fields. In: Proc. AAAI Workshop Plan, Activity, and Intent Recognition, California (July 2007)
Gu, T., Wu, Z., Tao, X., Pung, H.K., Lu, J.: epSICAR: An Emerging Patterns based Approach to Sequential, Interleaved and Concurrent Activity Recognition. In: Proc. IEEE Int’l Conf. on Pervasive Computing and Communications (Percom 2009), Galveston, Texas (March 2009)
Lombriser, C., Bharatula, N.B., Roggen, D., Tröster, G.: On-body activity recognition in a dynamic sensor network. In: Proc. Int’l Conf. Body Area Networks, BodyNets (2007)
Fayyad, U., Irani, K.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proc. Int’l Joint Conf. on Artificial Intelligence, San Francisco (1993)
Dong, G.Z., Li, J.Y.: Efficient mining of emerging patterns: discovering trends and differences. In: Proc. ACM Int’l Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA, pp. 43–52 (August 1999)
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Gu, T., Wang, L., Chen, H., Liu, G., Tao, X., Lu, J. (2012). Mining Emerging Sequential Patterns for Activity Recognition in Body Sensor Networks. In: Sénac, P., Ott, M., Seneviratne, A. (eds) Mobile and Ubiquitous Systems: Computing, Networking, and Services. MobiQuitous 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29154-8_9
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DOI: https://doi.org/10.1007/978-3-642-29154-8_9
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