Continuous Activity Monitoring

We use smartphones and wearables to automatically detect adverse events for diabetes patients. Our Continuous Activity Monitoring (CAM) creates a patient day journey, depicting those activities, that happened in the context of hypoglycemic events. This happens fully automatic, without any need for manual logging from the user. This enables patients and health care practitioners to better understand the context around adverse events and improve therapy.  


automatic detection of life style events 

We are constantly improving our algorithm in order to automatically detect life style events that have an impact on blood glucose levels. These events include various physical activities like walking, running or cycling and other life style events like for examaple sleeping, commuting or travel. 



We are working on a project to predict and prevent hypoglycemia and hyperglycemia. Specifically, we aim to automatically classify reasons for past hypoglycemia and hyperglycemia events, predict future similar events and notify patients in real-time so they can take action to prevent them.

This project is co-financed via the European Regional Development Fund (ERDF).