The fast-growing costs of acute care are pushing the healthcare systems worldwide to a limit. Globally, we are coming to realize that we cannot afford to provide everybody with access to unlimited healthcare services in the light of current demographic changes. An alternative approach is emerging that focuses on “keeping people healthy” through primary and secondary prevention in all phases of life. This paradigm shift in the healthcare systems is demanding research in ambient, sensor-enhanced assistive technologies that “keep people outside of the hospital”. Therefore, a fast-growing interest exists for pervasive computing systems and ambient assistive technology that aim at ubiquitous health support of individuals in the home and community settings.

The talk will present several examples for associated research projects in the fields of sports and medicine. A particular example is the miLife research project. In this project, we i) implemented ambient sensors for physiological (ECG, EMG, ...) and biomechanical (accelerometer, gyroscope, ...) data recording, ii) used pervasive computing systems (e.g. in smartphones or smarthomes) for monitoring and signal processing, and iii) employed data base technology, machine learning algorithms, and simulation models in order to provide accurate information to sportsmen, patients, and caregivers in numerous applications that aimed at promoting healthy living and improving health care.

The talk will also present further research challenges that exist in the field of pervasive computing systems for ubiquitous health support. Example challenges are the required signal processing and machine learning algorithms that need to be computationally efficient yet sufficiently accurate, but also comprehensive data bases, simulative data analysis and holistic data mining strategies. The outlook of the presentation will focus on future research directions that aim at contributing to the above mentioned paradigm shift in global healthcare systems by the use of pervasive computing systems for ubiquitous health support.