The founding team

Benjamin Kern

M.Sc. – Biomedical engineering


Since 2015 research assistant at TH Lübeck and doctoral candidate at University of Lübeck.


Extensive knowledge in biomedical optics, sensors & machine learning.

Reza Behroozian

M.Sc. – Biomedical engineering


Since 2016 research assistant at TH Lübeck and doctoral candidate at University of Lübeck.


Expert for electronics as well as measurement technology.

Till Böhme

B.Sc. – Industrial Engineering


Over 4 years experience in business development in medical and safety



Lead responsibility for
business development

Stefan Müller

Prof. Dr. – Electrical engineering


Since 2010 Head of the Laboratory for Medical Sensor and Device Technology at TH Lübeck.


Many years of experience in the development of medical devices.

The idea of mobOx

We’ve been working on the development of optical methods for the determination of various blood parameters for several years within various research projects funded by the Federal Ministry of Education and Research and the Federal Ministry for Economic Affairs and Energy. During this time, a complex laboratory setup has been developed, with which the interaction of light with whole blood samples can be extensively analyzed without sample pre-treatment.

With this laboratory setup we measured hundreds of blood samples. The insights obtained were transferred to an extensive database, which in turn provides the basis for modeling using deep learning. By carefully adapting and optimizing the predictive models, we achieved extraordinary robustness even under varying environmental conditions, such as temperature fluctuations and mechanical vibrations.

The measurement method developed by us is therefore particularly suitable for mobile use. This gave rise to the idea of developing a small, compact device for emergency medical services that offers the accuracy and reliability of stationary laboratory equipment directly at the point of use, thus enabling earlier and better therapy. In this way, we want to reduce the rate and severity of health consequences in patients and improve their outcome.