Subproject D04
Subproject D04
Sensor-Based Movement and Sleep Analysis in Parkinson’s Disease
In D04, innovative, non-contact EmpkinS sensor technology using machine learning algorithms and multimodal reference diagnostics is evaluated using the example of Parkinson’s-associated sleep disorder patterns. For this purpose, body function parameters of sleep are technically validated with wearable sensor technology and non-contact EmpkinS sensor technology in comparison to classical polysomnography and correlated to clinical scales. In an algorithmic approach, multiparametric sleep parameters and sleep patterns are then evaluated in correlation to movement, respiratory, cardiovascular, and sleep phase regulation disorders. Thereby, using the combination of intelligent Machine Learning algorithms and contactless EmpkinS Sensor Technology facilitates unobtrusive sleep analysis. This opens up new possibilities, such as continuing long-term monitoring of sleep-related Parkinson’s symptoms improving diagnosis and treatment.
Contacts
Principal Investigators
Prof. Dr. Björn Eskofier
Principal Investigator
Prof. Dr. med. Jürgen Winkler
Principal Investigator
Doctoral Candidates
Daniel Krauß, M. Sc.
Doctoral Canidate
Others
PD Dr. Heiko Gaßner
Associated Member
Dr. Jelena Jukic
Clinician Scientist
Prof. Dr. Jochen Klucken
Associated Member
Dr.-Ing. Heike Leutheuser
PostDoc
Additional Information
Publications
- Jakob V, Küderle A, Klucken J, Eskofier B, Winkler J, Winterholler M, Gaßner H, Kluge F: Validation of a sensor-based gait analysis system with a gold-standard motion capture system in patients with parkinson’s disease Sensors (2021)
- Ullrich M, Mücke A, Küderle A, Roth N, Gladow T, Gaßner H, Marxreiter F, Klucken J, Eskofier B, Kluge F: Detection of unsupervised standardized gait tests from real-world inertial sensor data in Parkinson’s disease IEEE T NEUR SYS REH (2021)
- Ullrich M, Roth N, Küderle A, Richer R, Gladow T, Gaßner H, Marxreiter F, Klucken J, Eskofier B, Kluge F: Fall Risk Prediction in Parkinson’s Disease Using Real-World Inertial Sensor Gait Data IEEE T NEUR SYS REH (2022)
- Brückner S, Weber J, Michler F, Shanin N, Schober R, Hagelauer A, Weigel R, Gaßner H, Winkler J, Eskofier B, Vossiek M: A Wireless Joint Communication and Localization EMG-Sensing Concept for Movement Disorder Assessment IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology (2023)