Sensor-based movement and sleep analysis in Parkinson’s syndrome (D04)
Acronym: SFB 1483 EmpkinS D04
Project leader: Björn Eskofier, Jürgen Winkler
Project members: Falk Pulsmeyer, Jelena Jukic, Daniel Krauß
Start date: 1. July 2021
End date: 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
Project leader: Björn Eskofier, Jürgen Winkler
Project members: Falk Pulsmeyer, Jelena Jukic, Daniel Krauß
Start date: 1. July 2021
End date: 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
Abstract
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 poly-somnography and correlated to clinical scales. In an algorithmic approach, multiparametric sleep parameters and sleep patterns are then evalulated in correlation to movement, cardiovascular and sleep phase regulation disorders.
Publications
2022
- Krauß D., Richer R., Küderle A., Beilner J., Rohleder N., Eskofier B.:
Benchmarking of Sleep/Wake Detection Algorithms on a Large Cohort using Actigraphy, HRV, and Respiration Information
IEEE-EMBS International Conference on Biomedical and Health Informatics (Ioaninna, 26. September 2022 - 30. September 2022)
BibTeX: Download - Krauß D., Richer R., Küderle A., Rohleder N., Eskofier B.:
Towards Integration of Sleep Quality in Sports Monitoring – Improving Wearable Sleep Detection Algorithms through Respiratory Information
13th World Congress of Performance Analysis of Sport 2022 & 13th International Symposium on Computer Science in Sport 2022 (Wien, 10. September 2022 - 13. September 2022)
BibTeX: Download
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
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering (2021), p. 2103-2111
ISSN: 1534-4320
DOI: 10.1109/TNSRE.2021.3119390
URL: https://ieeexplore.ieee.org/abstract/document/9567680
BibTeX: Download