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Martin Ullrich, M. Sc.

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

Room: Room 01.026
Carl-Thiersch-Straße 2b
91052 Erlangen

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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

2019

2018

Note for article: “Detection of gait from continuous inertial sensor data using harmonic frequencies”:

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Innovationlab for Wearable und Ubiquitous Computing

Supervised Projects:

Term Title
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)