I am Group Leader of the Biomedical & Pervasive Systems research group at the Department of Electrical and Computer Engineering (ECE), Aarhus University, and Principal Investigator and research lead for several interdisciplinary research and innovation projects within digital health. In addition to my research activities, I teach in the Computer Engineering and Biomedical Engineering programmes at Aarhus University.
My research focuses on the development and clinical application of digital technologies for early detection, prevention and monitoring of disease. A central research area is automated screening and home monitoring combined with artificial intelligence and clinical decision support. The aim is to identify disease and risk earlier, enable more personalised care, and at the same time reduce unnecessary clinical contacts and use of healthcare resources.
A substantial part of my research is conducted in close collaboration with Aarhus University Hospital and other Danish and international hospitals. PRESIDE, WODIA, APS and PREPRED constitute a coherent programme of research and development within screening, prevention and monitoring of preeclampsia and hypertensive pregnancy complications. The projects combine clinical data, biomarkers, automated blood-pressure measurements, home monitoring and AI-based risk models with digital infrastructures designed for integration into clinical practice.
My research interests include telemedicine and telemonitoring, pervasive and distributed systems, Internet of Things, artificial intelligence and machine learning, interoperability, and clinical decision-support systems. A recurring theme is the development of complete digital health systems – from sensors, self-service measurement stations, patient applications and wearable devices to data infrastructure, algorithms and integration with existing clinical information systems.
Together with colleagues at Aarhus University and national and international collaborators, we develop open and reusable technologies, tools and research platforms for supporting clinical research and subsequent implementation. Our approach is predominantly sensor- and data-driven and spans both hospital and home settings, with particular emphasis on clinical impact, patient safety, usability, interoperability and scalable implementation.
Beyond pregnancy-related healthcare, we apply the same principles in areas such as ambient assisted living and digital health monitoring, where ambient sensors, smartphones, wearables and medical devices are used to detect changes in health and functional status and to support patients, citizens and healthcare professionals through monitoring, decision support and coaching.
More information about Biomedical & Pervasive Systems (au.dk)
I am Head of the Healthcare Technology specialisation within the Master of Science in Computer Engineering programme at Aarhus University. The specialisation combines computer engineering, software development, artificial intelligence, sensor technologies and distributed systems with applications in healthcare and clinical technology.
I also teach in the Bachelor of Engineering in Healthcare Technology programme, the Bachelor of Science in Computer Engineering programme, and the MSc in Biomedical Engineering programme.
My teaching is closely connected to my research in digital health, telemedicine, telemonitoring, pervasive computing, and the development of clinical information and decision-support systems. I place particular emphasis on project-based learning, where students work with real-world healthcare challenges and technologies.
I teach, among others, the courses Telemonitoring, Advanced Telemedicine, Pervasive Computing, and Computer Engineering Project II.
I have supervised more than 100 bachelor project students, more than 50 MSc thesis students, 3 research-year students, and 3 PhD students.
