Abstract
Human behavior learning plays an important role in ambient assisted living since it enables service personalization. Current work in human behavior learning do not consider the context under which a behavior occurs, which hides some behaviors that are frequent only under certain conditions. In this work, we present the notion of a contextualized behavior pattern, which describes a behavior pattern with the context in which it occurs (i.e. nap when raining) and propose an algorithm for finding these patterns in a data stream. This is our main contribution. These patterns help to better understand the routine of a user in a smart environment, as is evidenced when testing with a public dataset. This algorithm could be used to learn behaviors from users in an ambient assisted living environment in order to send alarms when behavior changes occur.
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Resperate is a device used to lower blood pressure.
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Lago, P., Jiménez-Guarín, C., Roncancio, C. (2015). Contextualized Behavior Patterns for Ambient Assisted Living. In: Salah, A., Kröse, B., Cook, D. (eds) Human Behavior Understanding. Lecture Notes in Computer Science(), vol 9277. Springer, Cham. https://doi.org/10.1007/978-3-319-24195-1_10
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