Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)


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. 2 a
91052 Erlangen

Research Fields

Machine Learning and Data Analytics

Related Project(s)

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Applications of Deep Learning for Signal Analysis
Prof. Dr. Björn Eskofier
(01/07/2018 - 30/06/2021)

Open Badges: An Open-Source Sensor Platform for Analysis of Social Interactions and Group Dynamics
Prof. Dr. Björn Eskofier; Prof. Dr. Alex Pentland
(30/05/2018 - 30/11/2018)

HOOP: mHealth tOol for parkinsOn’s disease training and rehabilitation at Patient’s home
Prof. Dr. Björn Eskofier
(01/02/2018 - 31/01/2019)

Gait analysis in geriatrics using mobile sensor systems and machine learning for fall prediction
Prof. Dr. Björn Eskofier; Prof. Dr. Jochen Klucken
(15/01/2018 - 15/01/2021)

FallRiskPD: Fall risk detection for Parkinson's disease via intelligent gait analysis
Prof. Dr. Björn Eskofier; Prof. Dr. Jochen Klucken; Prof. Dr. Jürgen Winkler
(01/01/2018 - 31/12/2019)

Publications (Download BibTeX)

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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., 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.
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.
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
Timotius, I., Canneva, F., Minakaki, G., Pasluosta, C.F., Moceri, S., Casadei, N.,... Eskofier, B. (2018). Dynamic footprint based locomotion sway assessment in α-synucleinopathic mice using Fast Fourier Transform and Low Pass Filter. Journal of Neuroscience Methods, 296, 1-11. https://dx.doi.org/10.1016/j.jneumeth.2017.12.004
Wanner, P., Schmautz, T., Kluge, F., Eskofier, B., Pfeifer, K., & Steib, S. (2018). Unterschiede in der Sprunggelenksvariabilität beim Laufen zwischen Sportlern mit chronischer Instabilität und Copern. Paper presentation at 2. GAMMA Kongress, Hamburg, DE.
Klamroth, S., Gaßner, H., Winkler, J., Eskofier, B., Klucken, J., Pfeifer, K., & Steib, S. (2018). Sensomotorisches Laufbandtraining in der Rehabilitation von Gangstörungen bei Patienten mit Morbus Parkinson. In Deutsche Rentenversicherung Bund (Eds.), 27. Rehabilitationswissenschaftliches Kolloquium: Rehabilitation bewegt! (pp. 356-357).
Haji Ghassemi, N., Hannink, J., Martindale, C., Gaßner, H., Müller, M., Klucken, J., & Eskofier, B. (2018). Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson’s Disease. Sensors.
von Tscharner, V., Ullrich, M., Mohr, M., Comaduran Marquez, D., & Nigg, B.M. (2018). A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns. PLoS ONE. https://dx.doi.org/10.1371/journal.pone.0195125
Stoffel Antunes, R., Adams Seewald, L., Facco Rodrigues, V., Andre da Costa, C., da Silveira Junior, L.G., da Rosa Righi, R.,... Campanatti, G. (2018). A Survey of Sensors in Healthcare Workflow Monitoring. ACM Computing Surveys, 51(2). https://dx.doi.org/10.1145/3177852
Steib, S., Klamroth, S., Gaßner, H., Pasluosta, C.F., Eskofier, B., Winkler, J.,... Pfeifer, K. (2018). Gait adaptations to treadmill training in Parkinson’s patients: effects of perturbation, sustainability, and moderators. In Murphy, M., Boreham, C., De Vito, G., Tsolakidis, E. (Eds.), Book of Abstracts (pp. 335).
Richer, R., Zhao, N., Amores, J., Eskofier, B., & Paradiso, J.A. (2018). Real-time Mental State Recognition using a Wearable EEG. In IEEE (Eds.), . Honolulu, Hawaii, US.

Last updated on 2018-07-06 at 04:30