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


Open Badges: Eine Open-Source Plattform zur Analyse von sozialen Interaktionen und Gruppendynamik
Prof. Dr. Björn Eskofier; Prof. Dr. Alex Pentland
(30.05.2018 - 30.11.2018)


VR Amblyopie Trainer
Prof. Dr. Björn Eskofier
(01.04.2018 - 31.12.2019)


HOOP: mHealth tOol for parkinsOn’s disease training and rehabilitation at Patient’s home
Prof. Dr. Björn Eskofier
(01.02.2018 - 31.01.2019)


Ganganalyse bei geriatrischen Patienten mittels mobiler Sensorsysteme und maschinellem Lernen zur Prädiktion des Sturzrisikos
Prof. Dr. Björn Eskofier; Prof. Dr. Jochen Klucken
(15.01.2018 - 15.01.2021)



Publikationen (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.
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
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. (Unpublished, Accepted).
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
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.
Nowak, T., Hartmann, M., Thielecke, J., Hadachik, N., & Mutschler, C. (2018). Super-Resolution in RSS-based Direction-of-Arrival Estimation. In IEEE (Eds.), . Nantes, FR.
Stefke, A., Wilm, F., Richer, R., Gradl, S., Eskofier, B., Forster, C., & Namer, B. (2018). "MigraineMonitor" – Towards a System for the Prediction of Migraine Attacks using Electrostimulation. In Current Directions in Biomedical Engineering (pp. 629 - 632). Aachen, DE: De Gruyter.
Maurer, M., Kautz, T., Schlenzig, A., Hiemann, A., Zrenner, M., & Eskofier, B. (2018, September). Classification of Match Phases in Handball. Poster presentation at 12. Symposium der Sektion Sportinformatik und Sporttechnologie der Deutschen Vereinigung für Sportwissenschaft (dvs), München, DE.
Ullrich, M., Gladow, T., Roth, N., Küderle, A., Ollenschläger, M., Gaßner, H.,... Eskofier, B. (2018, July). FallRiskPD: Long-term fall risk classification for Parkinson’s disease via intelligent sensor-based gait analysis in the home environment (Talk). Paper presentation at European Falls Festival 2018, Manchester, GB.
Wirth, M., Gradl, S., Poimann, D., Schaefke, H., Matlok, S., Koerger, H., & Eskofier, B. (2018). Assessment of Perceptual-Cognitive Abilities among Athletes in Virtual Environments: Exploring Interaction Concepts for Soccer Players. In ACM New York, NY, USA ©2018 (Eds.), Proceedings of the 2018 Designing Interactive Systems Conference (pp. 1013-1024). Hong Kong, HK: New York.
Timotius, I., Canneva, F., Minakaki, G., Moceri, S., Casadei, N., Riess, O.,... Klucken, J. (2018). Systematic Data Analysis and Data Mining in Gait Analysis by Heat Mapping. In Robyn Grant, Tom Allen, Andrew Spink (Eds.), Measuring Behavior 2018. Manchester, GB: UK.
Timotius, I., Moceri, S., Plank, A.-C., Habermeyer, J., Canneva, F., Casadei, N.,... von Hörsten, S. (2018). Rodent’s Stride Length Depends on Body Size: Implications for CatWalk Assay. In Robyn Grant, Tom Allen, Andrew Spink, Matthew Sullivan (Eds.), Measuring Behavior 2018. Manchester, GB: UK.
Ring, M. (2018). Quantitative Estimation of Total Body Water Loss During Physical Exercise (Dissertation).
Timotius, I., Canneva, F., Minakaki, G., Pasluosta, C.F., Moceri, S., Casadei, N.,... Eskofier, B. (2018). Dynamic footprints of α-synucleinopathic mice recorded by CatWalk gait analysis. Data in Brief, 17, 189-193. https://dx.doi.org/10.1016/j.dib.2017.12.067
Moceri, S., Canneva, F., Habermeyer, J., Dobner, J., Schulze-Krebs, A., Puchades, M.,... von Hörsten, S. (2018, March). Association of early phenotypic behavioral alterations in human alpha-synuclein overexpressing transgenic rats with alpha-synuclein/huntingtin/amyloid-beta protein cross-seeding. Poster presentation at Advances in Alzheimer's and Parkinson's Therapies an AAT-AD/PD Focus Meeting, Torino, IT.
Hannink, J., Kautz, T., Pasluosta, C.F., Barth, J., Schülein, S., Gassmann, K.-G.,... Eskofier, B. (2018). Mobile Stride Length Estimation with Deep Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics, 22(2), 354 - 362. https://dx.doi.org/10.1109/JBHI.2017.2679486
Zrenner, M., Ullrich, M., Zobel, P., Jensen, U., Laser, F., Groh, B.,... Eskofier, B. (2018). Kinematic parameter evaluation for the purpose of a wearable running shoe recommendation. In IEEE (Eds.), Proceedings of the 15th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN) (pp. 106-109). Las Vegas, US.
Maurer, M., Zrenner, M., Reynolds, D., Dümler, B., & Eskofier, B. (2018). Sleeve Based Knee Angle Calculation for Rehabilitation. In IEEE (Eds.), Proceedings of the 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN) (pp. 1-4). Las Vegas, Nevada, US.
Amores, J., Richer, R., Zhao, N., Maes, P., & Eskofier, B. (2018). Promoting Relaxation Using Virtual Reality, Olfactory Interfaces and Wearable EEG. In IEEE (Eds.), 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN). Las Vegas, NV, US.
Pasluosta, C.F., Hannink, J., Gaßner, H., von Tscharner, V., Winkler, J., Klucken, J., & Eskofier, B. (2018). Motor output complexity in Parkinson's disease during quiet standing and walking: Analysis of short-term correlations using the entropic half-life. Human movement science, 58, 185-194. https://dx.doi.org/10.1016/j.humov.2018.02.005

Zuletzt aktualisiert 2018-04-09 um 10:57