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

 

Students

If you are interested in writing a Bachelor’s or Master’s thesis in our group, please check the lab’s Student Theses and Jobs.

  • David Rock
    Using Smart Devices to Assess the Health Status of Palliative Care Patients by Monitoring Activities of Daily Living

 

Projects