Martin Ullrich
Martin Ullrich, M. Sc.
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 |
2024
Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device
In: Scientific Reports 14 (2024), Article No.: 1754
ISSN: 2045-2322
DOI: 10.1038/s41598-024-51766-5
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Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study
In: JMIR Formative Research 8 (2024), Article No.: e50035
ISSN: 2561-326X
DOI: 10.2196/50035
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Gaitmap – An Open Ecosystem for IMU-based Human Gait Analysis and Algorithm Benchmarking
In: IEEE Open Journal of Engineering in Medicine and Biology (2024), p. 1-10
DOI: 10.1109/OJEMB.2024.3356791
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Correction to: Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium (Journal of NeuroEngineering and Rehabilitation, (2023), 20, 1, (78), 10.1186/s12984-023-01198-5)
In: Journal of neuroEngineering and rehabilitation 21 (2024), Article No.: 71
ISSN: 1743-0003
DOI: 10.1186/s12984-024-01361-6
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2023
Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium
In: Journal of neuroEngineering and rehabilitation 20 (2023), Article No.: 78
ISSN: 1743-0003
DOI: 10.1186/s12984-023-01198-5
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Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning
In: Orphanet Journal of Rare Diseases 18 (2023), Article No.: 249
ISSN: 1750-1172
DOI: 10.1186/s13023-023-02854-8
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Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization
In: Scientific Data 10 (2023), Article No.: 38
ISSN: 2052-4463
DOI: 10.1038/s41597-023-01930-9
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Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases
In: Frontiers in Neurology 14 (2023), Article No.: 1247532
ISSN: 1664-2295
DOI: 10.3389/fneur.2023.1247532
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Fall Risk Prediction in Parkinson's Disease Using Real-World Inertial Sensor Gait Data.
In: IEEE Journal of Biomedical and Health Informatics 27 (2023), p. 319-328
ISSN: 2168-2194
DOI: 10.1109/JBHI.2022.3215921
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uTUG: An unsupervised Timed Up and Go test for Parkinson's disease
In: Biomedical Signal Processing and Control 81 (2023), Article No.: 104394
ISSN: 1746-8094
DOI: 10.1016/j.bspc.2022.104394
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2022
Real-World Stair Ambulation Characteristics Differ Between Prospective Fallers and Non-Fallers in Parkinson’s Disease
In: IEEE Journal of Biomedical and Health Informatics (2022), p. 1-9
ISSN: 2168-2194
DOI: 10.1109/JBHI.2022.3186766
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Design and validation of a multi-task, multi-context protocol for real-world gait simulation
In: Journal of neuroEngineering and rehabilitation 19 (2022)
ISSN: 1743-0003
DOI: 10.1186/s12984-022-01116-1
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Fall Risk Prediction in Parkinson’s Disease Using Real-World Inertial Sensor Gait Data
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering (2022)
ISSN: 1534-4320
DOI: 10.1109/jbhi.2022.3215921
URL: https://ieeexplore.ieee.org/document/9924545
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2021
Consensus based framework for digital mobility monitoring
In: PLoS ONE 16 (2021)
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0256541
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Technical validation of real-world monitoring of gait: a multicentric observational study
In: BMJ Open 11 (2021)
ISSN: 2044-6055
DOI: 10.1136/bmjopen-2021-050785
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BioPsyKit: A Python package for the analysis of biopsychological data
In: Journal of Open Source Software 6 (2021), p. 3702
ISSN: 2475-9066
DOI: 10.21105/joss.03702
URL: https://www.theoj.org/joss-papers/joss.03702/10.21105.joss.03702.pdf
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Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson’s disease patients
In: Journal of neuroEngineering and rehabilitation 18 (2021), Article No.: 93
ISSN: 1743-0003
DOI: 10.1186/s12984-021-00883-7
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Do We Walk Differently at Home? A Context-Aware Gait Analysis System in Continuous Real-World Environments
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (, 1. November 2021 - 5. November 2021)
DOI: 10.1109/EMBC46164.2021.9630378
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Machine learning-based distinction of left and right foot contacts in lower back inertial sensor gait data
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (, 1. November 2021 - 5. November 2021)
DOI: 10.1109/EMBC46164.2021.9630653
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Detection of unsupervised standardized gait tests from real-world inertial sensor data in Parkinson’s disease
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering (2021), p. 2103-2111
ISSN: 1534-4320
DOI: 10.1109/TNSRE.2021.3119390
URL: https://ieeexplore.ieee.org/abstract/document/9567680
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2020
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
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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
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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
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2019
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
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2018
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
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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
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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
DOI: 10.1109/bsn.2018.8329670
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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
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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
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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
Innovationlab for Wearable und Ubiquitous Computing
Supervised Projects:
Term | Title |
Summer Term 2021 | Dungineers |
Winter Term 2020 / 21 | Argus |
Winter Term 2019 / 20 | RheumASim |
Summer Term 2019 | Pen Mates |
Winter Term 2018 / 19 | Watchstock |
Summer Term 2018 | Smart Foosball Table |
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) |