This research group focuses on the interface between the human body and technology, especially in the field of diabetes. For example, we are investigating whether data that are permanently and automatically generated by modern cars (CAN bus data) can be used by means of artificial intelligence to detect hypoglycemia early and reliably (even faster than with conventional glucose sensors) while driving. In parallel, we are investigating whether such a principle can also be applied to modern smart watches and other devices. The human-computer interface is also the focus of the "Deep Vision" project, where we are establishing automated detection and quantification of food via smartphone, again using modern technology (depth sensors, artificial intelligence).

This research group is concerned with the challenge that patients with diabetes face when they want to engage in physical activity. On the one hand, we have been investigating the metabolic processes during physical activity for years and developed new approaches to improve the safety during exercise (particularly for the prevention of hypoglycemia and hyperglycemia) in (amateur) athletes with type 1 diabetes. We also have access to professional athletes with type 1 diabetes. At the same time, we are investigating new ways to make physical exercise more successful for people with type 2 diabetes. The playful aspect (gamification) and again the human-computer interface play an important role in this task.