Abstract
In the last years, techniques for activity recognition have attracted increasing attention. Among many applications, a special interest is in the pervasive e-Health domain where automatic activity recognition is used in rehabilitation systems, chronic disease management, monitoring of the elderly, as well as in personal well being applications. Research in this field has mainly adopted techniques based on supervised learning algorithms to recognize activities based on contextual conditions (e.g., location, surrounding environment, used objects) and data retrieved from body-worn sensors. Since these systems rely on a sufficiently large amount of training data which is hard to collect, scalability with respect to the number of considered activities and contextual data is a major issue. In this paper, we propose the use of ontologies and ontological reasoning combined with statistical inferencing to address this problem. Our technique relies on the use of semantic relationships that express the feasibility of performing a given activity in a given context. The proposed technique neither increases the obtrusiveness of the statistical activity recognition system, nor introduces significant computational overhead to real-time activity recognition. The results of extensive experiments with data collected from sensors worn by a group of volunteers performing activities both indoor and outdoor show the superiority of the combined technique with respect to a solely statistical approach. To the best of our knowledge, this is the first work that systematically investigates the integration of statistical and ontological reasoning for activity recognition.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chang, K.H., Liu, S.Y., Chu, H.H., Hsu, J.Y.J., Chen, C., Lin, T.Y., Chen, C.Y., Huang, P.: The Diet-Aware Dining Table: Observing Dietary Behaviors over a Tabletop Surface. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 366–382. Springer, Heidelberg (2006)
Tentori, M., Favela, J.: Activity-Aware Computing for Healthcare. IEEE Pervasive Computing 7(2), 51–57 (2008)
Amft, O., Tröster, G.: Recognition of dietary activity events using on-body sensors. Artificial Intelligence in Medicine 42(2), 121–136 (2008)
Oliver, N., Horvitz, E., Garg, A.: Layered Representations for Human Activity Recognition. In: Proc. of ICMI-2002, IEEE Comp. Soc., pp. 3–8 (2002)
Brdiczka, O., Crowley, J.L., Reignier, P.: Learning Situation Models for Providing Context-Aware Services. In: Stephanidis, C. (ed.) UAHCI 2007 (Part II). LNCS, vol. 4555, pp. 23–32. Springer, Heidelberg (2007)
Golding, A.R., Lesh, N.: Indoor Navigation Using a Diverse Set of Cheap, Wearable Sensors. In: Proc. of ISWC-1999, IEEE Comp. Soc., pp. 29–36 (1999)
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)
Lester, J., Choudhury, T., Kern, N., Borriello, G., Hannaford, B.: A Hybrid Discriminative/Generative Approach for Modeling Human Activities. In: Proc. of IJCAI-2005, P.B.C., pp. 766–772 (2005)
Liao, L., Fox, D., Kautz, H.A.: Location-Based Activity Recognition using Relational Markov Networks. In: Proc. of IJCAI-2005, P.B.C, pp. 773–778 (2005)
Wang, S., Pentney, W., Popescu, A.M., Choudhury, T., Philipose, M.: Common Sense Based Joint Training of Human Activity Recognizers. In: Proc. of IJCAI-2007, pp. 2237–2242 (2007)
Stikic, M., Huynh, T., Laerhoven, K.V., Schiele, B.: ADL Recognition Based on the Combination of RFID and Accelerometer Sensing. In: Proc. of Pervasive Health 2008, IEEE Comp. Soc., pp. 2237–2242 (2008)
Huynh, T., Schiele, B.: Towards Less Supervision in Activity Recognition from Wearable Sensors. In: Proc. of ISWC 2006, IEEE Comp. Soc., pp. 3–10 (2006)
Pareschi, L., Riboni, D., Agostini, A., Bettini, C.: Composition and Generalization of Context Data for Privacy Preservation. In: Proc. of PerCom 2008 Workshops, IEEE Comp. Soc., pp. 429–433 (2008)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2008)
Horrocks, I., Patel-Schneider, P.F., van Harmelen, F.: From SHIQ and RDF to OWL: The making of a Web Ontology Language. Journal of Web Semantics 1(1), 7–26 (2003)
Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003)
Le Cessie, S., van Houwelingen, J.: Ridge Estimators in Logistic Regression. Applied Statistics 41(1), 191–201 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Riboni, D., Bettini, C. (2009). Context-Aware Activity Recognition through a Combination of Ontological and Statistical Reasoning. In: Zhang, D., Portmann, M., Tan, AH., Indulska, J. (eds) Ubiquitous Intelligence and Computing. UIC 2009. Lecture Notes in Computer Science, vol 5585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02830-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-02830-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02829-8
Online ISBN: 978-3-642-02830-4
eBook Packages: Computer ScienceComputer Science (R0)