Julius Hannink | MaD Lab

Dr.-Ing. Julius Hannink

Alumnus

Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik

Room 01.012
Carl-Thiersch-Straße 2b
91052 Erlangen

Phone number: +49 9131 85-27921
Email: julius.hannink@fau.de
Website: https://www.mad.tf.fau.de/person/julius-hannink/

2015 — 2018	PhD candidate in the Machine Learning and Data Analytics Lab (formerly Digital Sports Group) at the Friedrich-Alexander-University Erlangen-Nüremberg, Germany
2014	Research consultant for Wolfram Research Inc. M.Sc. degree in physics at the University of Göttingen, Germany
2013	Research internship at University Eye Clinic Maastricht and Eindhoven University of Technology, the Netherlands
2011 — 2012	International Student at University of Copenhagen, Denmark Research Assistant in the medical image analysis group at DIKU
2011	B.Sc. degree in physics at the University of Göttingen, Germany

EFI Moves

2020

Ivanović MD., Hannink J., Ring M., Baronio F., Vukčević V., Hadžievski L., Eskofier B.:
Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design
In: Artificial Intelligence in Medicine 110 (2020), Article No.: 101963
ISSN: 0933-3657
DOI: 10.1016/j.artmed.2020.101963
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2019

Gladow T., Gaßner H., Ullrich M., Hannink J., Roth N., Marxreiter F., Küderle A., Kohl Z., Winkler J., Eskofier B., Klucken J.:
Sensor-based gait analysis distinguishes fallers from non-fallers in Parkinson's disease under clinical and real-life conditions
BibTeX: Download
Haji Ghassemi N., Hannink J., Roth N., Gaßner H., Marxreiter F., Klucken J., Eskofier B.:
Turning analysis during standardized test using on-shoe wearable sensors in parkinson’s disease
In: Sensors 19 (2019), Article No.: 3103
ISSN: 1424-8220
DOI: 10.3390/s19143103
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Nguyen A., Roth N., Haji Ghassemi N., Hannink J., Seel T., Klucken J., Gaßner H., Eskofier B.:
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)
In: Journal of neuroEngineering and rehabilitation 16 (2019), Article No.: 98
ISSN: 1743-0003
DOI: 10.1186/s12984-019-0567-z
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Nguyen A., Roth N., Haji Ghassemi N., Hannink J., Seel T., Klucken J., Gaßner H., Eskofier B.:
Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson's disease
In: Journal of neuroEngineering and rehabilitation 16 (2019)
ISSN: 1743-0003
DOI: 10.1186/s12984-019-0548-2
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2018

Haji Ghassemi N., Hannink J., Martindale C., Gaßner H., Müller M., Klucken J., Eskofier B.:
Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson's Disease.
In: Sensors 18 (2018)
ISSN: 1424-8220
DOI: 10.3390/s18010145
BibTeX: Download
Haji Ghassemi N., Hannink J., Martindale C., Gaßner H., Müller M., Klucken J., Eskofier B.:
Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson’s Disease
In: Sensors (2018)
ISSN: 1424-8220
DOI: 10.3390/s18010145
URL: http://www.mdpi.com/1424-8220/18/1/145
BibTeX: Download
Hannink J., Kautz T., Pasluosta CF., Barth J., Schülein S., Gaßmann KG., Klucken J., Eskofier B.:
Mobile Stride Length Estimation with Deep Convolutional Neural Networks.
In: IEEE Journal of Biomedical and Health Informatics 22 (2018), p. 354 - 362
ISSN: 2168-2194
DOI: 10.1109/JBHI.2017.2679486
URL: https://www.mad.tf.fau.de/files/2017/06/Hannink-et-al.-2017-Mobile-Stride-Length-Estimation-with-Deep-Convolutional-Neural-Networks-1.pdf
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Kluge F., Hannink J., Pasluosta CF., Klucken J., Gaßner H., Gelse K., Eskofier B., Krinner S.:
Pre-operative sensor-based gait parameters predict functional outcome after total knee arthroplasty
In: Gait & Posture 66 (2018), p. 194-200
ISSN: 0966-6362
DOI: 10.1016/j.gaitpost.2018.08.026
URL: https://www.mad.tf.fau.de/files/2020/12/kluge_2018_gp_proof.pdf
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Martindale C., Roth N., Hannink J., Sprager S., Eskofier B.:
Smart Annotation Tool for Multi-sensor gait based daily activity data
In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) 2018
DOI: 10.1109/PERCOMW.2018.8480193
URL: https://www.mad.tf.fau.de/files/2018/09/percom2018_martindale.pdf
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Pasluosta CF., Hannink J., Gaßner H., von Tscharner V., Winkler J., Klucken J., Eskofier B.:
Motor output complexity in Parkinson's disease during quiet standing and walking: Analysis of short-term correlations using the entropic half-life.
In: Human movement science 58 (2018), p. 185-194
ISSN: 1872-7646
DOI: 10.1016/j.humov.2018.02.005
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Rafael Orozco-Arroyave J., Vasquez Correa J., Francisco Vargas-Bonilla J., Arora R., Dehak N., Nidadavolu PS., Christensen H., Rudzicz F., Yancheva M., Chinaei H., Vann A., Vogler N., Bocklet T., Cernak M., Hannink J., Nöth E.:
NeuroSpeech: An open-source software for Parkinson's speech analysis
In: Digital Signal Processing 77 (2018), p. 207-221
ISSN: 1051-2004
DOI: 10.1016/j.dsp.2017.07.004
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2017

Hannink J., Gaßner H., Winkler J., Eskofier B., Klucken J.:
Inertial sensor-based estimation of peak accelerations during heel-strike and loading as markers of impaired gait patterns in PD patients
10. Deutscher Kongress für Parkinson und andere Bewegungsstörungen (Baden-Baden, Germany, 4. May 2017 - 6. May 2017)
DOI: 10.1016/j.baga.2017.02.002
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Hannink J., Kautz T., Pasluosta CF., Gaßmann KG., Klucken J., Eskofier B.:
Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks.
In: IEEE Journal of Biomedical and Health Informatics 21 (2017), p. 85--93
ISSN: 2168-2194
DOI: 10.1109/JBHI.2016.2636456
URL: https://www.mad.tf.fau.de/files/2017/06/Hannink-et-al.-2017-Sensor-Based-Gait-Parameter-Extraction-With-Deep-Convolutional-Neural-Networks.pdf
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Hannink J., Kluge F., Gaßner H., Klucken J., Eskofier B.:
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), 2017, p. 129-132
DOI: 10.1109/BSN.2017.7936024
URL: http://ieeexplore.ieee.org/document/7936024/
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Hannink J., Ollenschläger M., Kluge F., Roth N., Klucken J., Eskofier B.:
Benchmarking Foot Trajectory Estimation Methods for Mobile Gait Analysis.
In: Sensors 17 (2017)
ISSN: 1424-8220
DOI: 10.3390/s17091940
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Kautz T., Groh B., Hannink J., Jensen U., Strubberg H., Eskofier B.:
Activity recognition in beach volleyball using a Deep Convolutional Neural Network
In: Data Mining and Knowledge Discovery 31 (2017), p. 1678–1705
ISSN: 1573-756X
DOI: 10.1007/s10618-017-0495-0
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Kluge F., Gaßner H., Hannink J., Pasluosta CF., Klucken J., Eskofier B.:
Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters
In: Sensors 17 (2017), p. 1522
ISSN: 1424-8220
DOI: 10.3390/s17071522
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Pasluosta CF., Steib S., Klamroth S., Gaßner H., Goßler J., Hannink J., Von Tscharner V., Pfeifer K., Winkler J., Klucken J., Eskofier B.:
Acute Neuromuscular Adaptations in the Postural Control of Patients with Parkinson’s disease after Perturbed Walking
In: Frontiers in Aging Neuroscience 9 (2017), p. 316
ISSN: 1663-4365
DOI: 10.3389/fnagi.2017.00316
URL: https://www.frontiersin.org/articles/10.3389/fnagi.2017.00316/full
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2016

Duits R., Janssen MHJ., Hannink J., Sanguinetti GR.:
Locally Adaptive Frames in the Roto-Translation Group and Their Applications in Medical Imaging
In: Journal of Mathematical Imaging and Vision (2016), p. 1-36
ISSN: 1573-7683
DOI: 10.1007/s10851-016-0641-0
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Pasluosta CF., Steib S., Klamroth S., Gaßner H., Hannink J., Von Tscharner V., Pfeifer K., Winkler J., Klucken J., Eskofier B.:
Motor Output Complexity in Parkinson's disease Patients during Acute Treadmill Intervention
International Symposium on the Neuromechanics of Human Movement (Heidelberg)
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Ter Haar Romeny BM., Bekkers EJ., Zhang J., Abbasi-Sureshjani S., Huang F., Duits R., Dashtbozorg B., Berendschot TTJM., Smit-Ockeloen I., Eppenhof KAJ., Feng J., Hannink J., Schouten J., Tong M., Wu H., van Triest HW., Zhu S., Chen D., He W., Xu L., Han P., Kang Y.:
Brain-inspired algorithms for retinal image analysis
In: Machine Vision and Applications (2016), p. 1-19
ISSN: 1432-1769
DOI: 10.1007/s00138-016-0771-9
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2014

Hannink J., Duits R., Bekkers E.:
Crossing-preserving multi-scale vesselness
In: Lecture Notes in Computer Science, Cambridge, MA: , 2014, p. 603-610 (Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014)
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Hannink J., Duits R., Bekkers EJ.:
Crossing-preserving multi-scale vesselness
Medical Image Computing and Computer-Assisted Intervention MICCAI 2014 (Cambridge, MA, 14. September 2014 - 18. September 2014)
In: Lecture Notes in Computer Science 2014
DOI: 10.1007/978-3-319-10470-6_75
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2014/Hannink14-CMV.pdf
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