Making P-Health Feasible: Automatic Detection of Personal Behavioural Changes through Process Mining Analysis Techniques

Abstract

With the arrival of Internet of Things (IoT) and Ambient Assisted Living frameworks(AAL), the amount of data available to analyze users in an individualized way is increasing dramatically. The big quantity of the available sensors allows the creation of holistic precise individualized models for explaining the user behavior, enabling the creation of better personalized treatments that can be adapted to user behavioral changes. Pattern recognition and machine learning techniques can create automatically behavioral models. However, for allowing a complete understanding of the individual behavior of a user it is desirable to create human understandable models in an automatic way. In this paper, we analyze the elegibility of Process Mining technologies for inferring behavioral models based on the available results on IoT and AAL frameworks.