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 on 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.