Abstract
Smart home has become a very active topic of research in the past few years. The problem of recognizing activity inside a smart home is one of the biggest challenge researchers have to face in this discipline. Many of them have presented approaches exploiting temporal constraints in order to maximize the efficiency and the precision of their recognition model. However, only few works investigate the spatial aspects characterizing the habitat context. In this paper, we present a new algorithm and a complete experiment showing the importance of taking into account spatial constraints in the recognition process. The goal of this paper is to demonstrate that recognition algorithms will benefit from exploiting spatial constraints.
This is a preview of subscription content, log in via an institution to check access.
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
Ramos, C., Augusto, J.C., Shapiro, D.: Ambient Intelligence: the Next Step for Artificial Intelligence. IEEE Intelligent Systems 23, 5–18 (2008)
Bouchard, B., Bouzouane, A., Giroux, S.: A Keyhole Plan Recognition Model for Alzheimer’s Patients: First Results, vol. 22, pp. 623–658. AAI, Taylor & Francis (2007)
Carberry, S.: Techniques for plan recognition. User Modeling and User-Adapted Interaction 11(1-2), 31–48 (2001)
Jakkula, V., Cook, D.: Using temporal relations in smart home data for activity prediction. In: Proc. of the ICML Workshop on the Induction of Process Models (2007)
Hoey, J., von Bertoldi, A., Poupart, P., Mihailidis, A.: Assisting persons with dementia during handwashing using a partially observable Markov decision process. In: Int. Conference on Vision Systems (ICVS 2007), pp. 89–99 (2007)
Patterson, D.J., Kautz, H.A., Fox, D., Lin, L.: Pervasive Computing in the Home and Community. In: Bardram, J.E., Mihailidis, A., Wan, D. (eds.), pp. 79–103. CRC Press, Boca Raton (2007)
Augusto, J.C., Nugent, C.D.: Smart Homes Can Be Smarter. In: Augusto, J.C., Nugent, C.D. (eds.) Designing Smart Homes. LNCS (LNAI), vol. 4008, pp. 1–15. Springer, Heidelberg (2006)
Egenhofer, M.J.: Point-Set Topological Spatial Relations. Internationnal Journal for Geographical Information Systems
Rocher, P.-O., Bouchard, B., Bouzouane, A.: A New Platform to Easily Experiment Activity Recognition Systems Based on RFID Tags. IJSH, SERSC,1-18 (2011) (accepted)
Quilllan, J.: C4.5: Programs for Machine Learning. M. Kaufmann, San Mateo (1993)
Schwartz, M.F., Segal, M., Veramonti, T., Ferraro, M., Buxbaum, L.J.: (NAT) The Naturalistic Action Test, Vol. 12(4), pp. 311–339 (August 2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bouchard, K., Bouchard, B., Bouzouane, A. (2011). Qualitative Spatial Activity Recognition Using a Complete Platform Based on Passive RFID Tags: Experimentations and Results. In: Abdulrazak, B., Giroux, S., Bouchard, B., Pigot, H., Mokhtari, M. (eds) Toward Useful Services for Elderly and People with Disabilities. ICOST 2011. Lecture Notes in Computer Science, vol 6719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21535-3_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-21535-3_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21534-6
Online ISBN: 978-3-642-21535-3
eBook Packages: Computer ScienceComputer Science (R0)