Prof. Dr. Björn Eskofier

Organisation

Awards / Honours

Project lead

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→ MOBILISE-D: Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement Prof. Dr. Björn Eskofier; Dr.-Ing. Felix Kluge (01/04/2019 - 31/03/2024)

→ (Digital Twin - holistische Beschreibung und Bewertung von Athleten): Digital Twin: Digital Twin – Novel data fusion algorithms and immersive interaction concepts for the holistic description and evaluation of athletes through self-learning systems Prof. Dr. Björn Eskofier (01/12/2018 - 31/05/2021)

→ Analysis and Modelling of p2p Security for Future Patient-centered Healthcare Ecosystem Prof. Dr. Björn Eskofier (01/10/2018 - 30/09/2021)

→ Classification of Acute Stress-Induced Response Patterns Prof. Dr. Björn Eskofier (01/09/2018)

→ Applications of Deep Learning for Signal Analysis Prof. Dr. Björn Eskofier (01/07/2018 - 30/06/2021)

Project member

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→ Human body odours: exploring chemical signatures Dr. Helene Loos (01/04/2019 - 31/03/2021)

→ Classification of Acute Stress-Induced Response Patterns Prof. Dr. Björn Eskofier (01/09/2018)

→ Applications of Deep Learning for Signal Analysis Prof. Dr. Björn Eskofier (01/07/2018 - 30/06/2021)

→ moveIT: A novel digital health pathway enables healthcare technologies for gait&falls in Parkinson’s disease Prof. Dr. Björn Eskofier; Prof. Dr. Jochen Klucken (01/01/2018 - 31/12/2018)

→ BayMed-mGL: Mobile GaITLab: Algorithmik für den Einsatz im Patientenalltag Prof. Dr. Jochen Klucken; Prof. Dr. Björn Eskofier (01/08/2017 - 31/01/2019)

Other Research Activities

Publications (Download BibTeX)

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→ Wanner, P., Schmautz, T., Kluge, F., Eskofier, B., Pfeifer, K., & Steib, S. (2019). Ankle angle variability during running in athletes with chronic ankle instability and copers. Gait & Posture, 68, 329–334.

→ Gradl, S., Wirth, M., Richer, R., Rohleder, N., & Eskofier, B. (2019). An Overview of the Feasibility of Permanent, Real-Time, Unobtrusive Stress Measurement with Current Wearables. In Proceedings of the EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth '19). Trento, IT.

→ Wanner, P., Schmautz, T., Kluge, F., Eskofier, B., Pfeifer, K., & Steib, S. (2019, January). Athletes with chronic ankle instability demonstrate altered ankle angle variability during running compared to copers. Paper presentation at Sportmotorik 2019. Adaptation, Lernen und virtuelle Welten, Bern, CH.

→ Abel, L., Richer, R., Küderle, A., Gradl, S., Eskofier, B., & Rohleder, N. (2019). Classification of Acute Stress-Induced Response Patterns. In ACM (Eds.), Proceedings of the EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth '19). Trento, IT.

→ Steib, S., Klamroth, S., Gaßner, H., Pasluosta, C.F., Eskofier, B., Winkler, J.,... Pfeifer, K. (2019). Effects of perturbed treadmill training on Parkinsonian gait: time-course, sustainability, and transfer effects. In Sportmotorik 2019. Adaptation, Lernen und virtuelle Welten. Abstractband zur 16. Jahrestagung der dvs-Sektion Sportmotorik vom 16.-18. Januar 2019 in Bern. Bern, CH.

→ Steib, S., Klamroth, S., Gaßner, H., Pasluosta, C.F., Eskofier, B., Winkler, J.,... Pfeifer, K. (2019). Exploring gait adaptations to perturbed and conventional treadmill training in Parkinson’s disease: Time-course, sustainability, and transfer. Human Movement Science. https://dx.doi.org/10.1016/j.humov.2019.01.007

→ Martindale, C., Sprager, S., & Eskofier, B. (2019). Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables. Sensors, 198. https://dx.doi.org/10.3390/s19081820

→ Dorschky, E., Krüger, D., Kurfess, N., Schlarb, H., Wartzack, S., Eskofier, B., & van den Bogert, A.J. (2019). Optimal control simulation predicts effects of midsole materials on energy cost of running. Computer Methods in Biomechanics and Biomedical Engineering. https://dx.doi.org/10.1080/10255842.2019.1601179

→ Maier, J., Black, M., Hall, M., Choi, J.-H., Levenston, M., Gold, G.,... Maier, A. (2019). Smooth Ride: Low-Pass Filtering of Manual Segmentations Improves Consensus. In Bildverarbeitung für die Medizin 2019 (pp. 86-91). Lübeck, DE: Springer Fachmedien Wiesbaden.

→ Gaßner, H., Raccagni, C., Eskofier, B., Klucken, J., & Wenning, G.K. (2019). The Diagnostic Scope of Sensor-Based Gait Analysis in Atypical Parkinsonism: Further Observations. Frontiers in Neurology, 10. https://dx.doi.org/10.3389/fneur.2019.00005