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.
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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
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DOI: https://doi.org/10.1007/978-3-642-12842-4_11
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