Research

Smart Wound Dressing incorporating Dye-based Sensors

In Germany alone, the number of patients with chronic wound healing disorders is estimated at around 2.7 million. The aim of the SWODDYS project is to research the fundamentals for a new type of intelligent wound dressing for the treatment of acute and chronic wounds, which can monitor the energy-metabolic tissue and wound healing status individually for each patient and online by integrating fluorescent dye-based oxygen, pH and CO2 sensors. 

This project is done in collaboration with PreSens, the University Hospital Regensburg, and Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) (Research group: Machine Learning and Data Analytics (MaD) Lab, Department AIBE) and is funded by the Bayerische Forschungsstiftung.

Diabetes Management

Children and adolescents with type 1 diabetes face a daily challenge: Maintaining stable blood glucose levels while managing the unpredictable effects of physical activity, stress, and growth. Despite advances in therapy, hypoglycemia remains the most common and most feared acute complication for these young patients and their families.
A major driver of this risk is daytime exercise, which has immediate and delayed glucose-lowering effects that vary widely depending on the type, duration, and intensity of the activity. Therefore, providing personalized treatment recommendations to the individual child remains a major clinical challenge.

Our research goals in this field are to improve blood glucose forecasting and nocturnal hypoglycemia prediction by specifically considering the physiological data of children and the effects of their daily routines. To overcome the limitations of standard continuous glucose monitoring, our research uses multimodal time series data. By applying advanced machine learning algorithms to these multivariate data streams, we aim to accurately forecast blood glucose dynamics, predict nocturnal hypoglycemia, and explore broader physiological patterns, such as deriving sleep phases and quantifying stress responses.

Our current work focuses on data from the recently completed DIAMonitor study. In this study, a comprehensive set of multimodal data, including continuous glucose levels, diverse physiological parameters, activity tracking, and dietary habits, was continuously recorded from young patients in their everyday home environment. We use these real-world insights to train our predictive models and better understand individual metabolic patterns.

This project is conducted in cooperation with Universitäts-Kinderspital beider Basel and ETH Zürich (Research group: Medical Data Science).