Abstract
In this paper we present an approach to the unsupervised recognition of activities of daily living (ADLs) in the context of smart environments. The developed system utilizes background domain knowledge about the user activities and the environment in combination with probabilistic reasoning methods in order to build best possible explanation of the observed stream of sensor events. The main advantage over traditional methods, e.g. dynamic Bayesian models, lies in the ability to deploy the solution in different environments without needing to undergo a training phase. To demonstrate this, tests with recorded data sets from two ambient intelligence labs have been conducted. The results show that even using basic semantic modeling of how the user behaves and how his/her behavior is reflected in the environment, it is possible to draw conclusions about the certainty and the frequencies with which certain activities are performed.
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
Munguia-Tapia, E., Choudhury, T., Philipose, M.: Building Reliable Activity Models Using Hierarchical Shrinkage and Mined Ontology. In: Proceedings of Pervasive 2006, Dublin (May 2006)
Munguia-Tapia, E.: Activity Recognition in the Home Setting Using Simple and Ubiquitous Sensors. Master Thesis, MIT (2003)
Pentney, W., Popescu, A., Wang, S., Kautz, H., Philipose, M.: Sensor-Based Understanding of Daily Life via Large-Scale Use of Common Sense. In: Proceedings of the National Conference on Artificial Intelligence (2006)
Wyatt, D., Philipose, M., Choudhury, T.: Unsupervised Activity Recognition Using Automatically Mined Common Sense. In: Proceedings of the National Conference on Artificial Intelligence (2005)
van Kasteren, T.L.M., Krse, B.J.A.: A probabilistic approach to automatic health monitoring for elderly. In: Proceedings of Advanced School of Computing and Imaging Conference (ASCI 2007), Heijen, The Netherlands (2007)
van Kasteren, T.L.M., Noulas, A.K., Englebienne, G., Krse, B.J.A.: Accurate Activity Recognition in a Home Setting. In: ACM Tenth International Conference on Ubiquitous Computing (Ubicomp 2008), Seoul, South Korea (2008)
van Kasteren, T.L.M., Englebienne, G., Krse, B.J.A.: Recognizing Activities in Multiple Contexts using Transfer Learning. In: AAAI Fall 2008 Symposium: AI in Eldercare (2008)
Wilson, D.: Assistive Intelligent Environments for Automatic Health Monitoring. PhD Thesis, Carnegie Mellon University (2005)
Oliver, N., Horvitz, E.: A Comparison of HMMs and Dynamic Bayesian Networks for Recognizing Office Activities. User Modeling (2005)
Dimitrov, T., Pauli, J., Naroska, E.: A probabilistic reasoning framework for smart homes. In: Proceedings of the 5th international workshop on Middleware for pervasive and ad-hoc computing: held at the ACM/IFIP/USENIX 8th International Middleware Conference
Dimitrov, T., Pauli, J., Naroska, E., Ressel, C.: Structured Learning of Component Dependencies in AmI Systems. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (2008)
Huang, C., Darwiche, A.: Inference in Belief Network: A Procedural Guide. International Journal of Approximate Reasoning (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dimitrov, T., Pauli, J., Naroska, E. (2010). Unsupervised Recognition of ADLs. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2010. Lecture Notes in Computer Science(), vol 6040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12842-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-12842-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12841-7
Online ISBN: 978-3-642-12842-4
eBook Packages: Computer ScienceComputer Science (R0)