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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(Entwicklung intelligenter neuronaler Netze zur Schrifterkennung):
EINNS: Entwicklung intelligenter neuronaler Netze zur Schrifterkennung
Prof. Dr. Björn Eskofier
(01.05.2019 - 30.04.2022)


DigiSportsHub: Digital Sports Hub: Ein Beitrag des deutschen Spitzensports für eine smarte Gesundheitsförderung
Prof. Dr. Björn Eskofier
(15.04.2019 - 15.10.2019)


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: Lernende Methoden mit Konfidenzmaß für die Digitalisierung manueller Arbeitsprozesse auf geringer Datengrundlage
Prof. Dr. Björn Eskofier
(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)



Publikationen (Download BibTeX)

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Timotius, I., Canneva, F., Minakaki, G., Moceri, S., Plank, A.-C., Casadei, N.,... von Hörsten, S. (2019). Systematic data analysis and data mining in CatWalk gait analysis by heat mapping exemplified in rodent models for neurodegenerative diseases. Journal of Neuroscience Methods, 326. https://dx.doi.org/10.1016/j.jneumeth.2019.108367
Feigl, T., Roth, D., Gradl, S., Wirth, M., Latoschik, M.E., Eskofier, B.,... Mutschler, C. (2019). Sick Moves! Motion Parameters as Indicators of Simulator Sickness. IEEE Transactions on Visualization and Computer Graphics. https://dx.doi.org/10.1109/TVCG.2019.2932224
Nguyen, A., Roth, N., Haji Ghassemi, N., Hannink, J., Seel, T., Klucken, J.,... Eskofier, B. (2019). Correction to: Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson's disease (J Neuroeng Rehabil (2019) 16:77 DOI: 10.1186/s12984-019-0548-2). Journal of neuroEngineering and rehabilitation, 16(1). https://dx.doi.org/10.1186/s12984-019-0567-z
Haji Ghassemi, N., Hannink, J., Roth, N., Gaßner, H., Marxreiter, F., Klucken, J., & Eskofier, B. (2019). Turning analysis during standardized test using on-shoe wearable sensors in parkinson’s disease. Sensors, 19(14). https://dx.doi.org/10.3390/s19143103
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
Klein, J., Zrenner, M., Dümler, B., & Eskofier, B. (2019, June). Development of Algorithms for Computing Knee Stability Parameters Using a Sensor Equipped Knee Sleeve. Poster presentation at ORTHO – Kongress der Master, Orthopädie 4.0, Weiden, DE.
Schwertner, M.A., Rigo, S.J., Araujo, D.A., Silva, A.B., & Eskofier, B. (2019). Fostering natural language question answering over knowledge bases in oncology EHR. In Proceedings - IEEE Symposium on Computer-Based Medical Systems (pp. 501-506). Cordoba, ES: Institute of Electrical and Electronics Engineers Inc..
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
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.
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.
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
Schwinn, L., Haderlein, T., Nöth, E., & Maier, A. (2019). Impact of Pathologies on Automatic Age Estimation. In Deutsche Gesellschaft für Akustik e.V. (Eds.), Fortschritte der Akustik - DAGA 2019 (pp. 939-942). Rostock, DE: Rostock: Deutsche Gesellschaft für Akustik e.V. (DEGA).
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
Zrenner, M., Feldner, C., Jensen, U., Richer, R., Roth, N., & Eskofier, B. (2019). Evaluation of foot kinematics during endurance running on different surfaces in real-world environments.


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