Trade fair / April 21, 2026 - April 23, 2026
DMEA 2026
Connecting Digital Health.
We cordially invite you to visit us at the joint Fraunhofer stand:
Connecting Digital Health.
We cordially invite you to visit us at the joint Fraunhofer stand:
📃 Title: HealthView – Contactless Vital Data Acquisition at the Bedside
🕐 Program Slot: K010 – Wed, April 22, Stage 6.2, 12:55 – 13:55
🎤 Speaker: Dr.-Ing. Gerald Bieber
Messe Berlin
Berlin Exhibition Grounds
Fraunhofer Joint Booth
April 21, 2026 - April 23, 2026
Fraunhofer Institute for Computer Graphics Research IGD
Fraunhofer Institute for Computer Graphics Research – Rostock branch of the institute IGD
Accessing one’s own medical data still involves many barriers for patients. Although the electronic patient record establishes the fundamental possibility of digital access, the user interfaces are often complex and cumbersome.
Fraunhofer IGD and SIT are developing secure and interactive technologies at the ATHENE research center to enable patients to easily gain an overview of their own data, visually and interactively explore medical information, and selectively and transparently share relevant data with medical institutions.
The exhibit demonstrates these new interactive possibilities and explains the underlying data security concept.
Chronic kidney disease often develops over many years and is characterized by complex disease trajectories and numerous clinical parameters. Physicians must analyze large volumes of heterogeneous patient data from lab results, diagnoses, and treatment histories. RenalViz supports them with interactive visual analytics methods.
Developed by Fraunhofer IGD, the system enables the visual analysis of patient cohorts with chronic kidney disease. Medical data such as lab values, diagnoses, and comorbidities are integrated into interactive visualizations. Multiple views support different analytical perspectives: an overview highlights similarities between patients and makes cohorts visible, while detailed views display temporal developments of individual lab values and disease progression, allowing comparisons across multiple patients.
This enables physicians to identify patient groups with similar disease trajectories, compare medical parameters, and detect patterns in disease progression. RenalViz thus supports the analysis of complex patient data and can facilitate both clinical decision-making and medical research.
In hospitals and inpatient care, increasing documentation requirements and the continuous monitoring of high-needs patients place significant strain on medical and nursing staff.
This is where Fraunhofer IGD’s CareCam technology comes in. Modern sensor technology and data analysis support clinical and nursing care while simultaneously improving patient safety. At its core is a contactless, non-invasive measurement of vital signs and patient well-being, directly at the bedside—without electrodes, cuffs, or wearable devices.
By intelligently analyzing multispectral imaging data, vital parameters such as heart rate, respiratory rate, skin temperature, movement patterns, and positional changes can be captured in real time. This enables continuous monitoring without disturbing patients or interrupting care processes.
In addition to analyzing key vital parameters, the system can detect relevant events such as restless sleep, atypical breathing patterns, or attempts to get out of bed. These capabilities help caregivers and medical staff identify critical changes at an early stage, automate routine tasks, and improve care for patients requiring close monitoring.
At the same time, the technology increases comfort for patients because no body-attached sensors are required.
CareCam clearly demonstrates how contactless sensor technology can become a key component of digitally supported care—secure, precise, and practical for everyday use.
As part of the BMFTR-funded project RenalCare, Fraunhofer IGD, together with clinical and industrial partners, is developing AI-based software solutions for patient-specific decision support in the surgical treatment of kidney tumors. The focus lies on the automated analysis of medical imaging data to support preoperative planning for partial nephrectomies.
Using AI-driven segmentation and analysis methods, relevant anatomical structures such as the tumor, renal parenchyma, vascular system, and collecting system are extracted from CT data. Based on this imaging information, established nephrometry scores such as PADUA and C are automatically calculated to objectively assess perioperative and postoperative risks, as well as the suitability of different treatment options.
The results are provided in a structured format and prepared for integration into existing clinical workflows. The exhibit demonstrates how medical image processing and artificial intelligence can help automate complex evaluation processes, improve decision quality, and facilitate the use of standardized scoring systems in everyday clinical practice.