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.

  • Caspar Siemssen
    Development of a Mobile Application for Assessing Quality of Life in Breast Cancer Patients: Study on Usability and User Preferences
  • Florian Prümer
    Motivational UI: Enhancing Adherence and Engagement of Heart Failure Patients within a Digital Health Application
  • Jonas Gruber
    Influence of Different Stimulus Intensities of Cold Air on Driver Drowsiness
  • Julia Hafenbrak
  • Juliane Ort
    Enhancing User Engagement in a Breast Cancer Prevention App: User-Centered Design Features to Promote Preventive Behavior
  • Sabrina Berzins
    Increasing Health Literacy through Patient-centered Knowledge Transfer within a Digital Health Application

 

Projects

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011