Abstract
In the near future, the world’s population will be characterized by an increasing average age, and consequently, the number of people requiring for a special household assistance will dramatically rise. In this scenario, smart homes will significantly help users to increase their quality of life, while maintaining a great level of autonomy. This paper presents a system for Ambient Assisted Living (AAL) capable of understanding context and user’s behavior by exploiting data gathered by a pervasive sensor network. The knowledge inferred by adopting a Bayesian knowledge extraction approach is exploited to disambiguate the collected observations, making the AAL system able to detect and predict anomalies in user’s behavior or health condition, in order to send appropriate alerts to family members and caregivers. Experimental results performed on a simulated smart home prove the effectiveness of the proposed system.
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This work is partially supported by the grant DM. 46965 LATO CIPE2.
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De Paola, A. et al. (2017). A Context-Aware System for Ambient Assisted Living. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_44
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DOI: https://doi.org/10.1007/978-3-319-67585-5_44
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