Julius Hannink



Organisation


Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Lehrstuhl für Informatik 5 (Mustererkennung)
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Augenklinik


Project member


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)

EFIMoves: EFI Moves: Individualized Diagnosis and Treatment in motion
Prof. Dr. Jochen Klucken; Prof. Dr. Jürgen Winkler
(01/01/2014 - 31/12/2017)


Publications (Download BibTeX)

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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.
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
Hannink, J., Gaßner, H., Winkler, J., Eskofier, B., & Klucken, J. (2017). Inertial sensor-based estimation of peak accelerations during heel-strike and loading as markers of impaired gait patterns in PD patients. Baden-Baden, Germany.
Hannink, J., Kautz, T., Pasluosta, C.F., Barth, J., Schülein, S., Gassmann, K.-G.,... Eskofier, B. (2017). Mobile Stride Length Estimation with Deep Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics. https://dx.doi.org/10.1109/JBHI.2017.2679486
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
Hannink, J., Kluge, F., Gaßner, H., Klucken, J., & Eskofier, B. (2017). Quantifying postural instability in Parkinsonian gait from inertial sensor data during standardised clinical gait tests. In 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) (pp. 129-132).
Hannink, J., Ollenschläger, M., Kluge, F., Roth, N., Klucken, J., & Eskofier, B. (2017). Benchmarking Foot Trajectory Estimation Methods for Mobile Gait Analysis. Sensors, 17(9). https://dx.doi.org/10.3390/s17091940
Kautz, T., Groh, B., Hannink, J., Jensen, U., Strubberg, H., & Eskofier, B. (2017). Activity recognition in beach volleyball using a Deep Convolutional Neural Network. Data Mining and Knowledge Discovery, 31(6), 1678–1705. https://dx.doi.org/10.1007/s10618-017-0495-0
Kluge, F., Gaßner, H., Hannink, J., Pasluosta, C.F., Klucken, J., & Eskofier, B. (2017). Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters. Sensors, 17(7), 1522. https://dx.doi.org/10.3390/s17071522
Pasluosta, C.F., Steib, S., Klamroth, S., Gaßner, H., Goßler, J., Hannink, J.,... Eskofier, B. (2017). Acute Neuromuscular Adaptations in the Postural Control of Patients with Parkinson’s disease after Perturbed Walking. Frontiers in Aging Neuroscience, 9, 316. https://dx.doi.org/10.3389/fnagi.2017.00316

Last updated on 2017-31-07 at 10:59