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)


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)


Anwendung von Deep Learning für Signalanalysen
Prof. Dr. Björn Eskofier
(01.07.2018 - 30.06.2021)



Publikationen (Download BibTeX)

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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
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
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
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.
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
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.
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, 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.
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
Gorse, L., Löffler, C., Mutschler, C., & Philippsen, M. (2018). Optical Camera Communication for Active Marker Identification in Camera-based Positioning Systems. In Proceedings of the 15th Workshop on Positioning, Navigation and Communications (WPNC'18). Bremen, DE: IEEE Xplore.
Stöve, M., & Eskofier, B. (2018, December). Last length estimation in football. Paper presentation at Spinfortec 2018, Garching b. München, DE.
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
Feigl, T., Mutschler, C., & Philippsen, M. (2018). Supervised Learning for Yaw Orientation Estimation. In Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2018). Nantes, FR: IEEE Xplore.
Feigl, T., Nowak, T., Philippsen, M., Edelhäußer, T., & Mutschler, C. (2018). Recurrent Neural Networks on Drifting Time-of-Flight Measurements. In Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2018). Nantes, FR: IEEE Xplore.
Ivanovic, M., Ring, M., Baronio, F., Calza, S., Vukcevic, V., Hadzievski, L.,... Eskofier, B. (2018). ECG derived feature combination versus single feature in predicting defibrillation success in out-of-hospital cardiac arrested patients. Biomedical Physics and Engineering Express, 5(1), 015012. https://dx.doi.org/10.1088/2057-1976/aaebec
Schellenberger, S., Shi, K., Mai, M., Wiedemann, J.P., Steigleder, T., Eskofier, B.,... Kölpin, A. (2018). Detecting Respiratory Effort-Related Arousals in Polysomnographic Data Using LSTM Networks. In Proceedings of the Computing in Cardiology. MECC Maastricht, NL.
Nitschke, M., Dorschky, E., Seifer, A.-K., Schlarb, H., van den Bogert, A.J., & Eskofier, B. (2018, September). Optimal Control Simulation of a 2D Biomechanical Model for Sensor-Based Gait Analysis. Poster presentation at Summer School "Humans in Motion", Heidelberg.


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 2018-04-09 um 10:57