AI x Bio Seminar: Radar Sensing for Contactless Physiological Monitoring During Sleep

[IN-PERSON AI × BIO SEMINAR IN MUNICH]
About the Talk
Join us for an AI × Bio seminar on "Radar Sensing for Contactless Physiological Monitoring During Sleep" with Daniel Krauss, researcher at the Institute for Artificial Intelligence in Medicine at LMU Munich, specializing in radar-based sensing of vital signs and contactless physiological monitoring using radar and machine learning.
What if we could monitor sleep without wearing sensors, attaching electrodes, or disturbing the person in bed? This talk explores how radar technology and machine learning can make sleep monitoring more unobtrusive by capturing subtle movements related to breathing, heartbeat, and body motion. Using REM sleep behavior disorder as an example, it shows how contactless sensing could support future approaches for detecting sleep-related markers of neurodegenerative diseases during natural sleep.
Agenda
19:00 – 20:00
Seminar presentation followed by Q&A
20:00 – 20:30
Networking session with food and drinks
About the Speaker
Daniel Krauss received his M.Sc. in Medical Engineering from FAU Erlangen-Nürnberg, where his master’s thesis at the Machine Learning and Data Analytics Lab focused on benchmarking sleep/wake detection algorithms using wearable sensors and machine learning. Motivated by this work, he continued his PhD at the same lab under the supervision of Prof. Björn Eskofier, shifting the focus toward contactless physiological monitoring during sleep using radar and machine learning.
Since May 2026, he has joined the Institute for Artificial Intelligence in Medicine at LMU Munich, where his research focuses on radar-based sensing of vital signs, including beat-to-beat cardiac monitoring, respiration analysis, and apnea detection in sleep and wake scenarios.
Daniel’s work driven by the idea that natural sleep remains under-investigated, despite its fundamental impact on health and daily life, and that unobtrusive monitoring technologies play an important role in early disease detection, tracking disease progression, and enabling clinically relevant long-term monitoring without interfering with patients.