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

Prof. Dr. Björn Eskofier

Principal Investigator

Prof. Dr. med. Jürgen Winkler

Principal Investigator

 

Daniel Krauß, M. Sc.

Doctoral Canidate

 

PD Dr. Heiko Gaßner

Associated Member

Prof. Dr. Jochen Klucken

Associated Member

 

 

Additional Information

  • Engel L., Müller J., Rendon EJF., Dorschky E., Krauß D., Ullmann I., Eskofier B., Vossiek M.:
    Advanced Millimeter Wave Radar-Based Human Pose Estimation Enabled by a Deep Learning Neural Network Trained With Optical Motion Capture Ground Truth Data
    IEEE Journal of Microwaves (2025), S. 1-15, ISSN: 2692-8388. DOI: 10.1109/JMW.2025.3535525
  • Krauß D., Richer R., Küderle A., Jukic J., German A., Leutheuser H., Regensburger M., Winkler J., Eskofier B.:
    Incorporating respiratory signals for machine learning-based multimodal sleep stage classification: a large-scale benchmark study with actigraphy and heart rate variability
    Sleep (2025), ISSN: 0161-8105. DOI: 10.1093/sleep/zsaf091
  • Albrecht N., Langer D., Krauß D., Richer R., Abel L., Eskofier B., Rohleder N., Kölpin A.:
    EmRad: Ubiquitous Vital Sign Sensing using Compact Continuous-Wave Radars.
    IEEE Open Journal of Engineering in Medicine and Biology (2024), S. 1-10. ISSN: 2644-1276. DOI: 10.1109/OJEMB.2024.3420241
  • Eskofier B., Vossiek M.:
    Invisible Sensing: Radar-based Biomonitoring.
    IEEE Open Journal of Engineering in Medicine and Biology (2024), S. 1-2. ISSN: 2644-1276. DOI: 10.1109/OJEMB.2024.3409086
  • Krauß D., Engel L., Ott T., Bräunig J., Richer R., Gambietz M., Albrecht N., Hille EM., Ullmann I., Braun M., Dabrock P., Kölpin A., Koelewijn A., Eskofier B., Vossiek M.:
    A Review and Tutorial on Machine Learning- Enabled Radar-Based Biomedical Monitoring.
    IEEE Open Journal of Engineering in Medicine and Biology (2024), S. 1-22. ISSN: 2644-1276. DOI: 10.1109/OJEMB.2024.3397208
  • Mejdani D., Bräunig J., Grießhammer S., Krauß D., Steigleder T., Engel L., Jukic J., Rozhdestvenskaya A., Winkler J., Eskofier B., Ostgathe C., Vossiek M.:
    Radar-Based Tremor Quantification Using Deep Learning for Improved Parkinson’s and Palliative Care Assessment.
    IEEE Transactions on Radar Systems 2 (2024), S. 1174-1185. ISSN: 2832-7357. DOI: 10.1109/TRS.2024.3494473
  • Müller J., Krauß D., Engel L., Vossiek M., Eskofier B., Dorschky E.:
    End-to-End Learning for Human Pose Estimation from Raw Millimeter Wave Radar Data.
    Asilomar Conference on Signals, Systems, and Computers (2024)
  • Lützow L., Teckenburg IB., Koch V., Marxreiter F., Jukic J., Stallforth S., Regensburger M., Winkler J., Klucken J., Gaßner H.:
    The effects of an individualized smartphone-based exercise program on self-defined motor tasks in Parkinson’s disease: a long-term feasibility study
    Journal of Patient-Reported Outcomes 7 (2023), Art.Nr.: 106, ISSN: 2509-8020. DOI: 10.1186/s41687-023-00631-6
  • Bräunig J., Mejdani D., Krauß D., Grießhammer S., Richer R., Schüßler C., Yip J., Steigleder T., Ostgathe C., Eskofier B., Vossiek M.:
    Radar-based Recognition of Activities of Daily Living in the Palliative Care Context Using Deep Learning.
    IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (Pittsburgh, 15 Oktober 2023 – 18 Oktober 2023). DOI: 10.1109/BHI58575.2023.10313506
  • Krauß D., Richer R., Albrecht NC., Küderle A., Abel L., Leutheuser H., Jukic J., German A., Kölpin A., Winkler J., Eskofier B.:
    Contactless Heart Rate Estimation using a 61 GHz Continuous-Wave Radar.
    IEEE-EMBS International Conference on Body Sensor Networks: Sensor and Systems for Digital Health (MIT Media Lab, Boston MA, 9 Oktober 2023 – 11 Oktober 2023)
  • Seifer AK., Popp F., Richer R., Gaßner H., Eskofier B.:
    Improving the Robustness of IMU-based Sit-To-Stand Detection during Simulated Real-World Activities for Different Sensor Positions.
    IEEE Biomedical and Health Informatics Conference (Pittsburgh, 15 Oktober 2023 – 18 Oktober 2023).
  • 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)
  • 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)
  • Schwinn L., Bungert L., Nguyen A., Raab R., Pulsmeyer F., Precup D., Eskofier B., Zanca D.:
    Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification.
    Proceedings of the 39th International Conference on Machine Learning (Baltimore, USA, 17 Juli 2022 – 23 Juli 2022). PMLR (Hrsg.): 162 2022. Open Access: https://proceedings.mlr.press/v162/schwinn22a.html
  • 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 Transactions on Neural Systems and Rehabilitation Engineering (2022). ISSN: 1534-4320
    DOI: 10.1109/jbhi.2022.3215921
  • Happold J., Richer R., Küderle A., Gaßner H., Klucken J., Eskofier B., Kluge F.:
    Evaluation of Orthostatic Reactions in Real-World Environments Using Wearable Sensors.
    43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (, 1 November 2021 – 5 November 2021)
    DOI: 10.1109/EMBC46164.2021.9630842
  • 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 21 (2021), Art.Nr.: 7680. ISSN: 1424-8220. DOI: 10.3390/s21227680
  • 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 Transactions on Neural Systems and Rehabilitation Engineering (2021), S. 2103-2111. ISSN: 1534-4320. DOI: 10.1109/TNSRE.2021.3119390