The CareCam medical plays a central role in this context. It enables the contactless acquisition of vital parameters such as pulse, respiratory rate, heart rate variability, and movement patterns using various sensors and AI-based analysis methods. The sensor system can be modularly mounted on the lifting pole of a care or hospital bed, enabling continuous monitoring without requiring additional devices to be attached directly to the patient’s body, as is common in many traditional monitoring approaches. Technologies used include camera-based systems for analyzing subtle changes in skin color, depth or LiDAR sensors for detecting breathing movements, and thermal imaging for analyzing surface temperatures. In addition, movement patterns in bed and changes in activity can be continuously recorded.

Multisensor Technology and AI Enable New Forms of Monitoring

The sensor data is continuously analyzed using AI. Characteristic patterns are extracted from individual measurement signals and combined. This makes it possible to detect changes in vital parameters or activity patterns that may indicate changes in health status.

“Contactless vital data acquisition opens up new possibilities for healthcare,” says Dr.-Ing. Gerald Bieber of Fraunhofer IGD. “It can be used both in home environments and, in the future, in hospitals, supporting caregivers with additional information about patients’ conditions.”

One possible application scenario is the support of care processes. Digital assistance systems can, for example, analyze activity patterns in bed, detect changes in respiration, or provide indications of potential health issues. This information can help caregivers prioritize tasks in their daily work and respond more quickly to critical situations.

Intelligent Assistance for Independent Living in Old Age

A practical example of this research is the newly launched, ZIM-funded project “SUSI – Smart and Secure AI-Based Assistance System for Independent Seniors.” The goal of the project is to develop an intelligent assistance system for the home environment that supports older people in their daily lives and detects changes in health conditions at an early stage.

SUSI combines various sensors with artificial intelligence methods and builds on technologies for contactless vital data acquisition. The system can analyze pulse, respiration, and movement patterns, among other things, and derive trends in health status. It also provides reminders for medication or medical appointments and, if desired, can inform relatives. The aim is not to provide medical diagnoses, but to make indications of potential changes visible at an early stage.

A particular focus is placed on protecting privacy. Sensor data is processed locally on the device as a rule, and users themselves decide which information is shared.

In the long term, such technologies could also help bridge the gap between home care and clinical care. If health data is already collected in everyday life, this information could provide additional insights into a patient’s health progression during hospital stays and support medical decision-making.

The SUSI project is being carried out in collaboration with Deep Care GmbH and Offenburg University of Applied Sciences. While Deep Care is developing a modular hardware platform, Offenburg University is working on user-friendly interaction concepts and an empathetic AI agent. Fraunhofer IGD contributes its expertise in contactless vital data acquisition and AI-based analysis of health data.

How these technologies can be applied in practice will also be demonstrated by Dr.-Ing. Gerald Bieber in a presentation at DMEA. Under the title “CareCam medical – Contactless Vital Data Acquisition at the Bedside,” he will present current developments of the CareCam technology and its applications in care and hospital settings.