Malte Seitz

Malte Seitz, M. Sc.

Researcher & PhD Candidate

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

Room: Room 01.012
Carl-Thiersch-Straße 2b
91052 Erlangen

Malte Seitz’s Research Focus

Wearable sensor systems using inertial measurement units (IMUs) enable objective and ubiquitous gait analysis. Being technically validated and able to report spatio-temporal gait parameters (e.g. stride length, stride time, rotation angles) with a high resolution, they are still not used in clinical practice, for example in geriatrics. Most clinicians use more traditional methods like questionnaires or macro gait parameters such as walked distance within a certain time as measured by stopwatches.

My research focuses on applications of IMU-based gait analysis in geriatric wards to bridge the gap between research and clinical practice. Closing this gap is important because researchers could use the tremendous amount of data that is generated in everyday practice and thus build reliable models and algorithms. Furthermore, closing the gap can accelerate the translation of the developed methods into innovation in medicine.