Martin Ullrich, M. Sc.
Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik
Carl-Thiersch-Straße 2b
91052 Erlangen
- Phone number: +49 9131 85 20159
- Email: martin.ullrich@fau.de
- Website: https://www.mad.tf.fau.de/person/martin-ullrich/
Research Focus
In many neurological disorders patients are suffering from impaired gait and mobility. Current clinical routine visits can usually not reflect the daily life health status of patients. For more objective observations we attach inertial measurement units (IMUs) to the patients’ shoes or lower back and obtain motion measurements over several days and weeks. My tasks relate to the analysis of the sensor raw data and the extraction of useful clinical information by developing algorithms and software. These can range from the counting of steps per day up to the estimation and prediction of fall risk. The ultimate goal of my research is to make the job of doctors easier and support patients with their disease.
- Algorithms for long-term, real-life gait analysis, especially of patients with Parkinson’s disease
- Machine-learning based estimation and prediction of fall risk
| Since 01/2018 | Researcher and Ph.D. student
Machine Learning and Data Analytics Lab Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuernberg (FAU) |
| 04/2017 – 09/2017 | Visiting Student
Human Performance Laboratory (HPL), University of Calgary Research internship with Master Thesis project, Supervisors: Benno Nigg, Vinzenz von Tscharner |
| 10/2015 – 12/2017 | Master’s Degree in Medical Engineering
Friedrich-Alexander-University Erlangen-Nuernberg (FAU) Master Thesis: Coherence and Pattern Analysis of Bipolar EMG-Currents during Running |
| 10/2011 – 09/2015 | Bachelor’s Degree in Medical Engineering
Friedrich-Alexander-University Erlangen-Nuernberg (FAU) Bachelor Thesis: “Recognition of Human Gait Using a Single Inertial- Magnetic Measurement Unit and Gait Specific Motion Models” Industrial internships at Dräger Medical GmbH in Lübeck and portabiles GmbH in Erlangen in cooperation with adidas AG in Herzogenaurach |
2020
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Automatic clinical gait test detection from inertial sensor data
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (Montreal, 20. July 2020 - 24. July 2020)
DOI: 10.1109/EMBC44109.2020.9176440
BibTeX: Download - , , , , , , , , :
Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies
In: Digital Biomarkers 4 (2020), p. 50-58
ISSN: 2504-110X
DOI: 10.1159/000511611
BibTeX: Download - , , , , , , , , :
Detection of Gait From Continuous Inertial Sensor Data Using Harmonic Frequencies
In: IEEE Journal of Biomedical and Health Informatics 24 (2020), p. 1869 - 1878
ISSN: 2168-2194
DOI: 10.1109/JBHI.2020.2975361
URL: https://www.mad.tf.fau.de/files/2020/11/2020_ullrich_gaitsequencedetection.pdf
BibTeX: Download
2019
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Sensor-based gait analysis distinguishes fallers from non-fallers in Parkinson's disease under clinical and real-life conditions
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Unsupervised harmonic frequency-based gait sequence detection for Parkinson’s disease
IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) (Chicago, 19. May 2019 - 22. May 2019)
DOI: 10.1109/BHI.2019.8834660
BibTeX: Download
2018
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FallRiskPD: Long-term fall risk classification for Parkinson’s disease via intelligent sensor-based gait analysis in the home environment (Talk)
European Falls Festival 2018 (Manchester, 2. July 2018 - 3. July 2018)
In: European Falls Festival, 2nd and 3rd July 2018, Manchester, United Kingdom, ABSTRACT BOOKLET 2018
URL: http://eufallsfest.eu/documents/Abstract Booklet.pdf
BibTeX: Download - , , , , :
Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units
In: Sensors 18 (2018), Article No.: 4194
ISSN: 1424-8220
DOI: 10.3390/s18124194
BibTeX: Download - , , , , , , , :
Kinematic parameter evaluation for the purpose of a wearable running shoe recommendation
Body Sensor Networks Conference (BSN) (Las Vegas, 4. March 2018 - 7. March 2018)
In: IEEE (ed.): Proceedings of the 15th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN) 2018
BibTeX: Download - , , , , :
A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns
In: PLoS ONE (2018)
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0195125
BibTeX: Download - , , , , :
Beta, gamma band, and high-frequency coherence of EMGs of vasti muscles caused by clustering of motor units
In: Experimental Brain Research (2018)
ISSN: 0014-4819
DOI: 10.1007/s00221-018-5356-6
BibTeX: Download
Note for article: “Detection of gait from continuous inertial sensor data using harmonic frequencies”:
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- 01/2012 – 12/2017: Scholarship from German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes)
- Fritz und Maria Hofmann-Preis (Technische Fakultät, Friedrich-Alexander-Universität Erlangen-Nürnberg) – 2019
| Year | Name | Title |
| 2020 | Annika Mücke |
Detection of semi-standardized gait tests from free-living inertial sensor recordings in Parkinson’s disease
(Bachelor’s Thesis) |
| 2019 | Lea Henrich |
Evaluation of IMU Orientation Estimation Algorithms Using a Three-Axis Gimbal
(Bachelor’s Thesis) |
| 2019 | Stefan Fischer |
Macro Analysis of free-living Gait in Parkinson’s Disease
(Bachelor’s Thesis) |
| 2019 | Kevin Rätsch | Clustering of physical activity patterns in COPD patients
(Bachelor’s Thesis) |