We develop methods to help healthcare professionals, patients, or people who want to stay healthy to understand often complex health-related data. So that they can make informed decisions leading to actions.
We specialise in methods that are data-driven. These are typically based on combinations of biomedical signal processing, (explainable) AI and ML and statistical analysis. Our methods are designed to work with real-life (‘ugly’) data that can be noisy, have artefacts and missing components. A guiding theme for us is that our methods should be well accepted by end-users and have an actual measurable impact and function in real-life.
As specific domain examples, we have several decades of expertise in critical care decision making (patient monitoring and diagnostics in ICU and OR) and chronic diseases (risk assessment, intervention planning and follow-up, with measurements at home and in hospital). We also have strong background in developing signal processing and analysis solutions for wearable health sensors. Furthermore, we actively contribute to health ICT standardization initiatives.
We carry out research collaboratively with domestic and international partners, including academic-, health- and social care, industrial- and patient representatives.
Besides state-of-the-art computing facilities, we have a laboratory facility available for measuring various bioelectric signals (e.g. ECG, EEG, EMG, EOG, GSR) with a wide range of monitoring devices. The laboratory is suitable, for example, for comparing health and wellness sensor devices against gold standard measurements, for sleep research, for sports and exercise research and for usability studies.