Digital Health – Biosignals
Acquiring and evaluating biomedical signals are essential aspects of the modern healthcare landscape. The Digital Health – Biosignals group addresses the acquisition of a variety of biosignals with wearables (e.g., smartwatches, smartphones) and intelligent processing and evaluation algorithms based on supervised, semi-supervised, and unsupervised machine learning approaches. The group is also developing novel machine learning algorithms integrated into innovative digital health support applications covering multiple components of healthcare, including health promotion and prevention, diagnosis, therapy, and rehabilitation/care. This includes the integration of human-computer interaction modalities.
Group Head
Group Members
Current Students
- Jonas Gruber
Influence of Different Stimulus Intensities of Cold Air on Driver Drowsiness - Michael Brock
Refinement, Feasibility and Evaluation of an eHealth Application for Opioid Management in Palliative Care - Nuria Barrios Campo
Retrospective Analysis of Self-reported Symptoms and Usage Data in a Digital Health Application
Running Projects
