Abstract
This paper proposes a context driven activity theory (CDAT) and reasoning approach for recognition of concurrent and interleaved complex activities of daily living (ADL) which involves no training and minimal annotation during the setup phase. We develop and validate our CDAT using the novel complex activity recognition algorithm on two users for three weeks. The algorithm accuracy reaches 88.5% for concurrent and interleaved activities. The inferencing of complex activities is performed online and mapped onto situations in near real-time mode. The developed systems performance is analyzed and its behavior evaluated.
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References
Philipose, M., Fishkin, K.P., Perkowitz, M., Patterson, D.J., Fox, D., Kautz, H., Hahnel, D.: Inferring activities from interactions with objects. IEEE Pervasive Computing 3, 50–57 (2004)
Choudhury, T., Consolvo, S., Harrison, B., Hightower, J., LaMarca, A., LeGrand, L., Rahimi, A., Rea, A., Bordello, G., Hemingway, B., Klasnja, P., Koscher, K., Landay, J.A., Lester, J., Wyatt, D., Haehnel, D.: The Mobile Sensing Platform: An Embedded Activity Recognition System. IEEE Pervasive Computing 7, 32–41 (2008)
Ferscha, A., Mattern, F., Tapia, E., Intille, S., Larson, K.: Activity Recognition in the Home Using Simple and Ubiquitous Sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)
Kaptelinin, V., Nardi, B., Macaulay, C.: Methods & tools: The activity checklist: a tool for representing the “space” of context. Interactions 6, 27–39 (1999)
Yang, G.-Z., Lo, B., Thiemjarus, S.: Body Sensor Networks. Springer, London (2006)
Albinali, F., Davies, N., Friday, A.: Structural Learning of Activities from Sparse Datasets. In: Fifth Annual IEEE International Conference on Pervasive Computing and Communications, pp. 221–228 (2007)
Davies, N., Siewiorek, D.P., Sukthankar, R.: Activity-Based Computing. IEEE Pervasive Computing 7, 20–21 (2008)
Tao, G., Zhanqing, W., Xianping, T., Hung Keng, P., Jian, L.: epSICAR: An Emerging Patterns based approach to sequential, interleaved and Concurrent Activity Recognition. In: IEEE International Conference on Pervasive Computing and Communications, pp. 1–9 (2009)
Rashidi, P., Cook, D., Holder, L., Schmitter-Edgecombe, M.: Discovering Activities to Recognize and Track in a Smart Environment. IEEE Transactions on Knowledge and Data Engineering 23(4), 527–539 (2011)
Dey, A.K.: Providing architectural support for building context-aware applications, PhD Thesis, Georgia Institute of Technology, p. 240 (2000)
Padovitz, A., Loke, S.W., Zaslavsky, A.: Towards a Theory of Context Spaces. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops (2004)
Bao, L., Intille, S.: Activity Recognition from User-Annotated Acceleration Data. In: Proc. Pervasive, Vienna, Austria, pp. 1–17 (2004)
Saguna, S., Zaslavsky, A., Chakraborty, D.: CrysP: Multi-Faceted Activity-Infused Presence in Emerging Social Networks. In: Balandin, S., Dunaytsev, R., Koucheryavy, Y. (eds.) ruSMART 2010. LNCS, vol. 6294, pp. 50–61. Springer, Heidelberg (2010)
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Saguna, Zaslavsky, A., Chakraborty, D. (2011). Complex Activity Recognition Using Context Driven Activity Theory in Home Environments. In: Balandin, S., Koucheryavy, Y., Hu, H. (eds) Smart Spaces and Next Generation Wired/Wireless Networking. ruSMART NEW2AN 2011 2011. Lecture Notes in Computer Science, vol 6869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22875-9_4
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DOI: https://doi.org/10.1007/978-3-642-22875-9_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22874-2
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