Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)


The chair of Computer Science 14 (Machine Learning and Data Analytics) was founded in 2018 and Prof. Björn Eskofier is the head of the Lab. The researchers in the Machine Learning and Data Analytics (MaD) lab conduct theoretical and applied research for wearable computing systems and machine learning algorithms for engineering applications at the intersection of sports and health care. Our motivation is generating a positive impact on human wellbeing, be it through increasing performance, maintaining health, improving rehabilitation, or monitoring disease.

Carl-Thiersch-Str. 2b
91052 Erlangen

Research Fields

Machine Learning and Data Analytics

Related Project(s)

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(Entwicklung intelligenter neuronaler Netze zur Schrifterkennung):
EINNS: Entwicklung intelligenter neuronaler Netze zur Schrifterkennung
Prof. Dr. Björn Eskofier
(01/05/2019 - 30/04/2022)

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 – 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)

Publications (Download BibTeX)

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Kurz, J., Tharmalingam, V., Pryakhina, N., Hunger, A., Ollenschläger, M., & Eskofier, B. (2019, July). A wearable obstacle detection system for visually impaired people. Poster presentation at 41st International Engineering in Medicine and Biology Conference, Berlin, DE.
Nguyen, A., Roth, N., Haji Ghassemi, N., Hannink, J., Seel, T., Klucken, J.,... Eskofier, B. (2019). Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson's disease. Journal of neuroEngineering and rehabilitation, 16. https://dx.doi.org/10.1186/s12984-019-0548-2
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
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.
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
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.
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.
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
Nitschke, M., Dorschky, E., Schlarb, H., Eskofier, B., van den Bogert, A.J., & Koelewijn, A.D. (2019). Turning a corner in predictive musculoskeletal simulations of gait using implicit dynamics. Poster presentation at Dynamic Walking 2019, Canmore, Alberta, CA.
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
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.
Jahn, K., Freiberger, E., Eskofier, B., Bollheimer, C., & Klucken, J. (2019). Balance and mobility in geriatric patients. Zeitschrift für Gerontologie und Geriatrie. https://dx.doi.org/10.1007/s00391-019-01561-z
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
da Rosa Righi, R., Andrioli, L., Facco Rodrigues, V., Andre da Costa, C., Marcos Alberti, A., & Singh, D. (2019). Elastic-RAN: An adaptable multi-level elasticity model for Cloud Radio Access Networks. Computer Communications, 34-47. https://dx.doi.org/10.1016/j.comcom.2019.04.012
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
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.

Publications in addition (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

Last updated on 2019-24-04 at 10:19