Extraction of User Daily Behavior From Home Sensors Through Process Discovery

Abstract

In the last years, the wide availability on the market of low-cost smart devices paved the way for the development of smart environments, which offer an unprecedented opportunity to recognize the patterns of activities from a large amount of collected data, with the ultimate aim of monitoring the user behavior. In this article, we propose a methodology which relies on process discovery techniques to analyze sensor data in terms of activation sequences and to discover process models representing user's behavioral patterns. The extraction of such models is valuable not only in the perspective of gaining a better insight on how a certain task is performed but also in supporting novel smart services. In order to evaluate the effectiveness of the approach, in this article, we also consider a real-world case study set in an ambient-assisted living environment.