  Process mining techniques aim to extract insights in processes from event
logs. One of the challenges in process mining is identifying interesting and
meaningful event labels that contribute to a better understanding of the
process. Our application area is mining data from smart homes for elderly,
where the ultimate goal is to signal deviations from usual behavior and provide
timely recommendations in order to extend the period of independent living.
Extracting individual process models showing user behavior is an important
instrument in achieving this goal. However, the interpretation of sensor data
at an appropriate abstraction level is not straightforward. For example, a
motion sensor in a bedroom can be triggered by tossing and turning in bed or by
getting up. We try to derive the actual activity depending on the context
(time, previous events, etc.). In this paper we introduce the notion of label
refinements, which links more abstract event descriptions with their more
refined counterparts. We present a statistical evaluation method to determine
the usefulness of a label refinement for a given event log from a process
perspective. Based on data from smart homes, we show how our statistical
evaluation method for label refinements can be used in practice. Our method was
able to select two label refinements out of a set of candidate label
refinements that both had a positive effect on model precision.
