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)

Go to first page Go to previous page 1 von 6 Go to next page Go to last page

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)

Go to first page Go to previous page 1 von 6 Go to next page Go to last page

Schellenberger, S., Shi, K., Mai, M., Wiedemann, J.P., Steigleder, T., Eskofier, B.,... Kölpin, A. (2019). Detecting Respiratory Effort-Related Arousals in Polysomnographic Data Using LSTM Networks. (Unpublished, Accepted).
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
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
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, 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.
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.
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.
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
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.


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