Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)


Beschreibung:


Der Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik) wurde 2018 gegründet und wird von Prof. Dr. Björn Eskofier geleitet. Die Wissenschaftler im Machine Learning and Data Analytics (Mad) Lab betreiben theoretische und angewandte Forschung mit Wearable Computing Systems und Machine Learning Algorithmen für technische Anwendungen an der Schnittstelle von Sport und Healthcare. Unsere Motivation ist es, einen positiven Einfluss auf menschliches Wohlbefinden zu erreichen, sei es durch Leistungssteigerung, Erhalt der Gesundheit, Verbesserung von Rehabilitation oder überwachung des Krankheitsstatus.

Adresse:
Carl-Thiersch-Str. 2b
91052 Erlangen


Forschungsbereiche

Maschinelles Lernen und Datenanalytik


Forschungsprojekt(e)

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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)


LZE KH: Leistungszentrum Elektroniksysteme kognitive Handwerkzeuge
Christoffer Löffler
(01.02.2019 - 31.12.2020)


(Digital Twin - holistische Beschreibung und Bewertung von Athleten):
Digital Twin: Digital Twin - Neuartige digitale Datenfusionsalgorithmen und immersive Interaktionskonzepte für die holistische Beschreibung und Bewertung von Athleten durch selbstlernende Systeme
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)


Klassifizierung von Stressreaktions-Mustern induziert durch akuten Stress
Prof. Dr. Björn Eskofier
(01.09.2018)



Publikationen (Download BibTeX)

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Facco Rodrigues, V., da Rosa Righi, R., Andre da Costa, C., Eskofier, B., & Maier, A. (2019). On Providing Multi-Level Quality of Service for Operating Rooms of the Future. Sensors, 19(10). https://dx.doi.org/10.3390/s19102303
Minakaki, G., Canneva, F., Chevessier, F., Bode, F., Menges, S., Timotius, I.,... Klucken, J. (2019). Treadmill exercise intervention improves gait and postural control in alpha-synuclein mouse models without inducing cerebral autophagy. Behavioural Brain Research, 363, 199-215. https://dx.doi.org/10.1016/j.bbr.2018.11.035
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.
Amores, J., Fuste, A., & Richer, R. (2019). Deep Reality: Towards Increasing Relaxation in VR by Subtly Changing Light, Sound and Movement Based on HR, EDA, and EEG. In ACM (Eds.), CHI 2019 Video Showcase. Glasgow, GB.
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.
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
Meyer, V., Facco Rodrigues, V., da Rosa Righi, R., Andre da Costa, C., Galante, G., & Bonato Both, C. (2019). Pipel: Exploiting Resource Reorganization to Optimize Performance of Pipeline-Structured Applications in the Cloud. International Journal of Computational Systems Engineering, 1. https://dx.doi.org/10.1504/IJCSYSE.2019.098414
Niitsoo, A., Edelhäußer, T., Hadaschik, N., Eberlein, E., & Mutschler, C. (2019). A Deep Learning Approach to Position Estimation from Channel Impulse Responses. Sensors, 19(5), 1-23. https://dx.doi.org/10.3390/s19051064
Aubreville, M., Stöve, M., Oetter, N., Goncalves, M., Knipfer, C., Neumann, H.,... Maier, A. (2019). Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images. International Journal of Computer Assisted Radiology and Surgery, 14(1), 31-42. https://dx.doi.org/10.1007/s11548-018-1836-1
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.
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.
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
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.
Adams Seewald, L., Facco Rodrigues, V., Ollenschläger, M., Stoffel Antunes, R., Andre da Costa, C., da Rosa Righi, R.,... Fahrig, R. (2019). Toward analyzing mutual interference on infrared-enabled depth cameras. Computer Vision and Image Understanding. https://dx.doi.org/10.1016/j.cviu.2018.09.010
da Rosa Righi, R., Facco Rodrigues, V., Fontana De Nardin, I., Andre da Costa, C., Antonio Zanata Alves, M., & Aronne Pillon, M. (2019). Towards providing middleware-level proactive resource reorganisation for elastic HPC applications in the cloud. International Journal of Grid and Utility Computing, 10(1), 76-92. https://dx.doi.org/10.1504/IJGUC.2019.097220
Jahn, K., Freiberger, E., Eskofier, B., Bollheimer, C., & Klucken, J. (2019). Balance and mobility in geriatric patients: Assessment and treatment of neurological aspects Gleichgewicht und Mobilität bei geriatrischen Patienten: Beurteilung und Behandlung neurologischer Aspekte. Zeitschrift für Gerontologie und Geriatrie. https://dx.doi.org/10.1007/s00391-019-01561-z
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
Gaßner, H., Steib, S., Klamroth, S., Pasluosta, C.F., Adler, W., Eskofier, B.,... Klucken, J. (2019). Perturbation Treadmill Training Improves Clinical Characteristics of Gait and Balance in Parkinson's Disease. Journal of Parkinson's Disease, 9(2), 413-426. https://dx.doi.org/10.3233/JPD-181534


Zusätzliche Publikationen (Download BibTeX)


Zrenner, M., Gradl, S., Jensen, U., Ullrich, M., & Eskofier, B. (2018). Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units. Sensors, 18(12). https://dx.doi.org/10.3390/s18124194
Kluge, F., Hannink, J., Pasluosta, C.F., Klucken, J., Gaßner, H., Gelse, K.,... Krinner, S. (2018). Pre-operative sensor-based gait parameters predict functional outcome after total knee arthroplasty. Gait & Posture, 66, 194-200. https://dx.doi.org/10.1016/j.gaitpost.2018.08.026
Hannink, J., Kautz, T., Pasluosta, C.F., Gaßmann, K.-G., Klucken, J., & Eskofier, B. (2017). Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics, 21(1), 85--93. https://dx.doi.org/10.1109/JBHI.2016.2636456
Kluge, F., & Eskofier, B. (2017). Letter to the Editor regarding "Gait recording with inertial sensors - How to determine initial and terminal contact" by Bötzel and colleagues. Journal of Biomechanics, 52, 183-184. https://dx.doi.org/10.1016/j.jbiomech.2016.07.043

Zuletzt aktualisiert 2019-24-04 um 10:19