Funded by: DFG

Period: 07/2021 – 12/2025

Cardiac Impedance Measurement for Improved Hemodynamic Monitoring

Funded by: BMWK (ZIM)

Period: 08/2022 - 04/2025

This ZIM project is about the integration of unobtrusive measurement technology for monitoring vital parameters, such as heart rate and respiratory rate, into an emergency bed. The aim of this project is to increase the coverage of vital sign monitoring in the emergency room for patients who do not require acute monitoring, and to improve the early detection of cardiovascular complications.

Funded by: BMBF

Period: tbd

Holistic inference system for AI-based assistance in the control of intensive care ventilators

Optimal therapies through data-driven decision-making and support systems

Funded by: DFG

Period: 11/2023 - 10/2026

The aim of this project is to develop a patient bed that can measure vital signs such as heart rate and respiratory rate unobtrusively. To achieve this, sensors will be integrated into the mattress, and cameras will be mounted above the patient's bed. Continuous monitoring of vital signs should enable early detection of atrial fibrillation.

Funded by: DFG

Period: 09/2020 – 02/2025

To improve the estimation and control of the interactive force between robot and users, a robust algorithm for the correct estimation of muscle force or muscle torque will be developed. In this project by combining electromyography (EMG) and impedance myography (EIM). This should provide a new quantitative assessment tool for muscle activity, describing in particular the interactive force / torque development between robot and user.

Funded by: BMBF

Period: 01/2025 - 12/2027

The NeuroSys (neuromorphic systems) project aims to make Germany a world-leading location for the development of neuromorphic hardware. To this end, many institutions from science (such as RWTH Aachen University), industry and society have joined forces to cover research and development as well as production, application and ethics. The fields of application of artificial intelligence are diverse, and intelligent systems are increasingly expected to be used in mobile devices. Instead of having to limit performance in such a case, the focus here is on the development of hardware that is to be explicitly designed for the implementation of neural networks ("neuromorphic") and will therefore be significantly more efficient than, for example, conventional general processing units (GPUs). The Chair of Medical Information Technology is working on the application of these novel structures in the field of camera- and deep learning-based diagnostics.

Funded by: DFG

Period: 07/2023 - 06/2026

The project is dedicated to modeling and removing physiological motion artifacts in capacitive ECG in order to improve signal quality and diagnostic power.

Funded by: DFG

Period: 10/2023 - 09/2026

The aim of the research project is to create a theoretical and practical basis for EIT measurements in the time domain and thus significantly expand the range of applications of EIT by utilising the advantages of time domain measurements.

Funded by: BMWK (ZIM)

Period: 01/2025 - 06/2027

The aim of this ZIM project is to develop a functionalised rollator that will lead to a leap in innovation in this market segment. Sensors are used to record vital parameters and analyse movement patterns during use. Based on this data, the drive power is automatically adapted to the support requirements. This approach addresses an adaptive biofeedback system that helps to maintain and gradually extend the user's mobility radius. In this way, the degree of independence and social participation is increased.

Funded by: BMWK (ZIM)

Period: 01/2023 - 06/2025

This project aims to develop a non-invasive monitoring system for arterial and venous blood to estimate oxygen consumption and further detect venous circulatory diseases. For that, photoplethysmography (PPG) is used, a technique in which the skin is illuminated with two specific wavelengths to distinguish the absorbing properties of oxygenated and deoxygenated hemoglobin. PPG is obtained using contact-based sensors and video images from a commercial webcam, enabling a spatially resolved blood dynamics analysis.