Activities of daily living are good indicators of the health status of elderly. Therefore, automating the monitoring of these activities is a crucial step in future care giving. However, many models for activity recognition rely on labeled examples of activities for learning the model parameters. Due to the high variability of different contexts, parameters learned for one context can not automatically be used in another. In this paper, we present a method that allows us to transfer knowledge of activity recognition from one context to the next, a task called transfer learning. We show the effectiveness of our method using real world datasets.
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