Semantic Process Mining for Ambient Assisted Living

Ambient Assisted Living (AAL) systems are aimed to assist elderly people and enhance their autonomy, by monitoring their health, supporting their daily activities and so on. AAL tools are employed for several purposes, e.g. medication management, social isolation prevention, fall detection. In this work, we focus on the analysis of daily activities of monitored people and, in particular, on the detection of common patterns of daily activities. These patterns allow to understand the habitual behavior of monitored people, that is a valuable knowledge both in order to enhance the support provided to elders in performing their activities and to be able to quickly detect unexpected or dangerous situations. However, AAL tools usually return data at a very low level of detail, analyzing which too detailed patterns are inferred, which are of scarce support for the human analyst. To address this issue, in this work, we discuss the application of a combined methodology based on the combination of semantic techniques and multidimensional analysis paradigm to allow the analyst to switch to the desired level of granularity and to consider different process perspective, thus enhancing the analysis.