Abstract
This paper presents a novel knowledge-driven approach to recognising multi-user concurrent activities in smart home environments. Capturing these concurrent activity patterns is challenging in that it usually requires detailed application-/user-specific specifications, or needs a large amount of data to build sophisticated models. The proposed approach is founded upon the use of a generic ontology model to represent domain knowledge, which is independent of particular sensor deployment and activities of interest. It leverages the hierarchical structure of domain concept ontologies and applies well-established hierarchy-based techniques to enable automatic segmentation of real-time sensor traces and supports matching finely grained sensor data to coarsely constrained activities. We empirically evaluate our approach using a large-scale real-world dataset, achieving an average accuracy of 86%.
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References
Cook, D., Schmitter-Edgecombe, M.: Assessing the quality of activities in a smart environment. Methods of Information in Medicine 48, 480–485 (2009)
Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing 5(4), 277–298 (2009)
Gong, S., Xiang, T.: Recognition of group activities using dynamic probabilistic networks. In: ICCV 2003, Washington, DC, USA, pp. 742–749 (2003)
Grauman, K., Darrell, T.: The pyramid match kernel: Efficient learning with sets of features. J. Mach. Learn. Res. 8, 725–760 (2007)
Gu, T., Chen, S., Tao, X., Lu, J.: An unsupervised approach to activity recognition and segmentation based on object-use fingerprints. Data Knowl. Eng. 69(6), 533–544 (2010)
Gu, T., Wang, X.H., Pung, H.K., Zhang, D.Q.: An ontology-based context model in intelligent environments. In: CNDS 2004, pp. 270–275 (January 2004)
Helaoui, R., Niepert, M., Stuckenschmidt, H.: Recognizing interleaved and concurrent activities: A statistical-relational approach. In: PERCOM 2011, pp. 1–9. IEEE Computer Society, Washington, DC (2011)
Hu, D.H., Yang, Q.: Cigar: concurrent and interleaving goal and activity recognition. In: Proceedings of the 23rd National Conference on Artificial Intelligence, AAAI 2008, vol. 3, pp. 1363–1368. AAAI Press (2008)
Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: Human activity recognition from wireless sensor network data: Benchmark and software. In: Chen, L., Nugent, C.D., Biswas, J., Hoey, J. (eds.) Activity Recognition in Pervasive Intelligent Environments. Atlantis Ambient and Pervasive Intelligence, vol. 4, ch. 8, pp. 165–186. Atlantis Press, Paris (2011)
Kim, E., Helal, S., Cook, D.: Human activity recognition and pattern discovery. IEEE Pervasive Computing 9(1), 48–53 (2010)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR 2006, pp. 2169–2178. IEEE Computer Society, Washington, DC (2006)
Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)
Modayil, J., Bai, T., Kautz, H.: Improving the recognition of interleaved activities. In: Proceedings of the 10th International Conference on Ubiquitous Computing, UbiComp 2008, pp. 40–43. ACM, New York (2008)
Nguyen, N.T., Venkatesh, S., Bui, H.: Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association. In: Proceedings of the British Machine Vision Conference, pp. 126.1–126.10 (2006)
Okeyo, G., Chen, L., Wang, H., Sterritt, R.: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive and Mobile Computing (2012)
Patterson, D.J., Fox, D., Kautz, H., Philipose, M.: Fine-grained activity recognition by aggregating abstract object usage. In: ISWC 2005, pp. 44–51. IEEE Computer Society (2005)
Riboni, D., Bettini, C.: Context-aware activity recognition through a combination of ontological and statistical reasoning. In: Zhang, D., Portmann, M., Tan, A.-H., Indulska, J. (eds.) UIC 2009. LNCS, vol. 5585, pp. 39–53. Springer, Heidelberg (2009)
Saguna, Zaslavsky, A., Chakraborty, D.: Recognizing concurrent and interleaved activities in social interactions. In: DASC 2011, pp. 230–237 (December 2011)
Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: ACL 1994, Stroudsburg, PA, USA, pp. 133–138 (1994)
Ye, J., Dobson, S., McKeever, S.: Situation identification techniques in pervasive computing: a review. Pervasive and Mobile Computing 8, 36–66 (2012)
Ye, J., Stevenson, G., Dobson, S.: A top-level ontology for smart environments. Pervasive and Mobile Computing 7, 359–378 (2011)
Yu, T.-H., Kim, T.-K., Cipolla, R.: Real-time action recognition by spatiotemporal semantic and structural forests. In: Proceedings of British Machine Vision Conference, Aberystwyth, UK, pp. 1–12. British Machine Vision Association (2010)
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Ye, J., Stevenson, G. (2013). Semantics-Driven Multi-user Concurrent Activity Recognition. In: Augusto, J.C., Wichert, R., Collier, R., Keyson, D., Salah, A.A., Tan, AH. (eds) Ambient Intelligence. AmI 2013. Lecture Notes in Computer Science, vol 8309. Springer, Cham. https://doi.org/10.1007/978-3-319-03647-2_15
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DOI: https://doi.org/10.1007/978-3-319-03647-2_15
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