Abstract
This paper presents a rule-based approach for both offline and real-time recognition of Activities of Daily Living (ADL), leveraging events produced by a non-intrusive multi-modal sensor infrastructure deployed in a residential environment. Novel aspects of the approach include: the ability to recognise arbitrary scenarios of complex activities using bottom-up multi-level reasoning, starting from sensor events at the lowest level; an effective heuristics-based method for distinguishing between actual and ghost images in video data; and a highly accurate indoor localisation approach that fuses different sources of location information. The proposed approach is implemented as a rule-based system using Jess and is evaluated using data collected in a smart home environment. Experimental results show high levels of accuracy and performance, proving the effectiveness of the approach in real world setups.
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References
Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)
Artikis, A., Sergot, M.J., Paliouras, G.: A logic programming approach to activity recognition. In: Scherp, A., Jain, R., Kankanhalli, M.S., Mezaris, V. (eds.) Proceedings of the 2nd ACM International Workshop on Events in Multimedia, EiMM 2010, pp. 3–8. ACM, New York (2010)
Chen, L., Khalil, I.: Activity recognition: approaches, practices and trends. In: Chen, L., Nugent, C.D., Biswas, J., Hoey, J. (eds.) Activity Recognition in Pervasive Intelligent Environments. Atlantis Ambient and Pervasive Intelligence, vol. 4, pp. 1–31. Atlantis Press, Paris (2011)
Chen, L., Nugent, C.D., Wang, H.: A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. Knowl. Data Eng. 24(6), 961–974 (2012)
Filippaki, C., Antoniou, G., Tsamardinos, I.: Using constraint optimization for conflict resolution and detail control in activity recognition. In: Keyson, D.V., Maher, M.L., Streitz, N., Cheok, A., Augusto, J.C., Wichert, R., Englebienne, G., Aghajan, H., Kröse, B.J.A. (eds.) AmI 2011. LNCS, vol. 7040, pp. 51–60. Springer, Heidelberg (2011)
Helaoui, R., Riboni, D., Stuckenschmidt, H.: A probabilistic ontological framework for the recognition of multilevel human activities. In: Mattern, F., Santini, S., Canny, J.F., Langheinrich, M., Rekimoto, J. (eds.) UbiComp 2013, pp. 345–354. ACM (2013)
Hill, E.F.: Jess in Action: Java Rule-Based Systems. Manning Publications Co., Greenwich (2003)
Kyriazakos, S., Mihaylov, M., Anggorojati, B., Mihovska, A., Craciunescu, R., Fratu, O., Prasad, R.: eWALL: an intelligent caring home environment offering personalized context-aware applications based on advanced sensing. Wirel. Pers. Commun. 87(3), 1093–1111 (2016)
Liu, J., Zhang, G., Liu, Y., Tian, L., Chen, Y.Q.: An ultra-fast human detection method for color-depth camera. J. Vis. Commun. Image Represent. 31, 177–185 (2015)
Maekawa, T., Yanagisawa, Y., Kishino, Y., Ishiguro, K., Kamei, K., Sakurai, Y., Okadome, T.: Object-based activity recognition with heterogeneous sensors on wrist. In: Floréen, P., Krüger, A., Spasojevic, M. (eds.) Pervasive 2010. LNCS, vol. 6030, pp. 246–264. Springer, Heidelberg (2010)
Meditskos, G., Dasiopoulou, S., Kompatsiaris, I.: MetaQ: a knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns. Pervasive Mob. Comput. 25, 104–124 (2016)
Riboni, D., Bettini, C.: COSAR: hybrid reasoning for context-aware activity recognition. Pers. Ubiquit. Comput. 15(3), 271–289 (2011)
Skarlatidis, A., Paliouras, G., Artikis, A., Vouros, G.A.: Probabilistic event calculus for event recognition. ACM Trans. Comput. Log. 16(2), 11:1–11:37 (2015)
Woznowski, P., Fafoutis, X., Song, T., Hannuna, S., Camplani, M., Tao, L., Paiement, A., Mellios, E., Haghighi, M., Zhu, N., et al.: A multi-modal sensor infrastructure for healthcare in a residential environment. In: 2015 IEEE International Conference on Communication Workshop, pp. 271–277. IEEE (2015)
Woznowski, P., King, R., Harwin, W., Craddock, I.: A human activity recognition framework for healthcare applications: ontology, labelling strategies, and best practice. In: Proceedings of the International Conference on Internet of Things and Big Data (IoTBD), pp. 369–377. INSTICC (2016)
Acknowledgments
This work was performed under the SPHERE IRC, funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant EP/K031910/1.
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Baryannis, G., Woznowski, P., Antoniou, G. (2016). Rule-Based Real-Time ADL Recognition in a Smart Home Environment. In: Alferes, J., Bertossi, L., Governatori, G., Fodor, P., Roman, D. (eds) Rule Technologies. Research, Tools, and Applications. RuleML 2016. Lecture Notes in Computer Science(), vol 9718. Springer, Cham. https://doi.org/10.1007/978-3-319-42019-6_21
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DOI: https://doi.org/10.1007/978-3-319-42019-6_21
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