Fraunhofer vs. Corona: Noncontact health monitoring of infectious patient groups

Corona pandemic: current situation

With the corona crisis, hospitals are being confronted with scores of highly infectious patients. The majority are not treated in intensive care units, but are monitored by medical personnel who manually check body temperatures and other vitals on a regular basis. The issue is that each personal contact with one of these patients increases the risk of infection among the health care personnel.

Portable monitoring devices can reduce these frequent visits because they monitor vital functions such as body temperature, oxygen saturation and respiration rate and forward the values to a central information system via wireless communication technology. The result is comprehensive, noncontact patient monitoring, which lessens the burden on the medical staff and offers them better protection against infections so that they can continue to care for the high number of patients.

Before it can be used in health care environments and with people who are ill, the system must function reliably. That means it has to be robust against interference and be able to function flawlessly with multiple patients at the same time. Fraunhofer IKS, together with Airbus, is thus evaluating an ear sensor already tested in the field to ensure that the system is robust enough for hospital environments. The system will also be tested for scalability and to determine whether it operates reliably in larger spaces – in other words in parallel with many other sensors.

Flying hospitals for treating and transporting patients

With the MedEvac machine, Airbus offers aircraft that serve as flying hospital wards. In early 2020 these German military aircraft were frequently used to fly COVID-19 patients out of Italy and France for treatment in Germany. Since the number of medical personnel is restricted on board the aircraft, in-ear sensors are used to monitor the patients’ vital functions. The medical staff can centrally monitor the condition of all of the passengers with a computer. This system is also suitable for hospitals that are overloaded as a result of COVID-19. Patients can be continuously monitored in normal wards to avoid unnecessary contact and infections.    

Our contribution: reliable health monitoring, even on a large scale

The base system comprising measurement instruments and experimental software will be provided by industrial partners. Researchers at Fraunhofer IKS are testing and optimizing the system in a lab environment. To guarantee robustness and scalability, IKS will work together with Airbus in the joint project to expand the basic platform - an ear sensor, an optional wireless EKG, a local recording node (RaspberryPI) and a dashboard for visualizing the data - with the required mechanisms.

Given that it will be deployed in safety-relevant areas, the system must fulfill the corresponding safety requirements. The researchers are evaluating these stringent safety demands by carrying out stress tests in the institute’s own reference system. Data from the field tests is also flowing into the tests.

The researchers are also examining to what extent the system can be scaled for larger applications and integrated into existing hospital systems. Beginning with a prototype, the project team will develop a scalable product capable of monitoring the vital functions of numerous patients and which is suitable for processing the large volumes of data. Since the software has so far been designed for only a small number of end devices, researchers are examining how the system reacts when a considerably higher number of end devices is simultaneously connected. The idea is to take advantage of the system’s potential under exceptional circumstances, such as the corona pandemic, as well as on a permanent basis as part of normal day-to-day hospital operations.

The new system promises to serve as a springboard for additional enhancements in collaboration with the project partners. The data that is captured can be automatically analyzed with AI-based methods and used to derive recommendations for streamlining the hospital’s operation.

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