Human Computer Interaction
Our group explores novel interaction concepts and models for wearable and ubiquitous systems. The aim is to provide intuitive and innovative interfaces for applications in sports, wellbeing and healthcare. Our research is focused on the improvement of cognitive skills, applications within the reality-virtuality continuum and approaches for biofeedback.
- Assessment and Improvement of Mental Health
- Diagnostic Imaging in Virtual Reality
- Digital Sports Bavaria
- Digital Vision Trainer
- Signal Analysis and Biofeedback
Our group is conducting research in machine learning as well as signal and event processing to develop analysis pipelines for sports, healthcare, industrial and educational applications. We are working on inertial and biomedical sensor data, tracking, imaging and recommender systems.
- Applications of Deep Learning for Signal Analysis
- EFI Moves
- Green Belt ML@Operations
- Information management system for automated quality assessment in radiotherapy
- Low-Power Electronics for Sports and Fitness Applications
- Performance Indicators for Professional Sports
- Performance Analysis in Team Sports
- Theoretical Machine Learning
- Autonomous Cranes
- Smart Annotation
- BayMED Mobile GaITLab
- BayMED Sleep Project
- HOOP: mHealth tOol for parkinsOn’s disease training and rehabilitation at Patient’s home
- Gait analysis in geriatrics using mobile sensor systems and machine learning for fall prediction
- Security for Future Patient-centered Healthcare Ecosystem
Modeling and Simulation
Our group investigates musculoskeletal modeling and simulation to analyze and understand human movement and performance. Our objective is to reconstruct human motion from measurement data for example for medical assessments or to predict human responses for virtual product development.
Our group develops and integrates hardware unobtrusively for applications in the fields of sports, wellbeing and healthcare. Embedded signal processing and classification of inertial sensors, biosignals and environmental data is used to analyze human movements, vital signs or to recognize activities and events. Making use of the growing computational power of Wearables, we are able to generate real-time feedback for athletes, to monitor patients or to support diagnosis.
- RoboCup Small Size League
- RoboCup Logistics League
- Data Mining in the U.S. National Toxicology Program (NTP) Database
- Noninvasive determination of the human hydration level