Embedding computational capability into quotidian objects is called ubiquitous computing and is a big trend in software engineering. These objects become capable of tracking their user’s actions by the use of various sensors that can collect many types of information, such as heart rate, location and movement.
All this information can be used in many ways and one of them is to compare past and present sensor metrics to establish connections and similarities between activities, with the ﬁnal objective of gaining knowledge about the user of the pervasive system.
Fraunhofer AICOS solutions in the ﬁeld of monitoring physical activity and related metrics store large amounts of data generated during the use of such applications by the users. The analysis of this data would lead to the understanding of user’s behaviour patterns, that could greatly improve the reach of the solutions already created. The lack of knowledge about the user’s behaviour patterns prevents the solutions previously mentioned from personalizing their suggestions, strategies and recommendations to improve their efﬁciency when applied to intervene in the user’s physical, cognitive or behaviour decline. It is quite important for caregivers to have a complete awareness of the user’s routines so they can correct the unhealthy ones. In this work the problem of using computational devices and algorithms to detect user’s routines is addressed by using frequent pattern mining techniques.
The main goal of this project is to develop activity pattern recognition tool that can be used for personalization of interventions to the user’s routines.
For any additional information regarding this project, please contact us using the inquiries form.