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5G healthcare applications

The Peter L. Reichertz Institute for Medical Informatics is part of the 5G Labratory Braunschweig Wolfsburg. Within this framework, we develop IoT applications to evaluate the potential of 5G for healthcare applications. Here you can find a short video clip (in German) introducing our aims. Preprints are already available regarding edge-driven ECG analysis (Arxiv) and the feasibility of 5G for the transmission of medical image data (Arxiv).

Accident & Emergency Informatics

I am involved in the research towards Accident & emergency informatics at the Peter L. Reichertz Institute for Medical Informatics, including the ISAN project. See our Methods Inf Med paper which introduces basic concepts.

Smart Home healthcare applications

The Peter L. Reichertz Institute for Medical Informatics hosts a ''smart home'' laboratory equipped by a multiude of unobstrusive sensors. We develop novel signal processing techniques to enable applications that address the challenges of an aging society. See our Sensors paper for an overview of the state-of-the-art. Recently, we began a study on capacitive ECG embedded into chairs with the protocol being published in PLos One.

Crowdsourcing in medical imaging

Machine learning is a promising approach in medical image processing but large amounts of training data are required and expert annotations are costly. We work towards establishing crowdsourcing as a cost-efficient alternative which also provides knowledge transfer.

Photoplethsymography imaging in medical imaging

During my PhD I developed methods for cardiac triggering/gating based on photoplethsymography imaging in the context of ultra-high-field magnetic resonance imaging. See my ISMRM 2017 slides and ISMRM 2018 poster for the idea and application, respectively.

Multi-scale parameter estimation (msPE)

msPE is a robust framework for computing parameters of Gaussian-related functions that I developed together with Prof. Dr. Markus Kukuk during my PhD. You can find an open-source implementation on Github and details in our IEEE Transactions on Signal Processing paper. See our IEEE Journal of Biomedical and Health Informatics paper for details on the application of msPE to ECG signals.