Authors:
Lorenz Grünerbel
;
Ferdinand Heinrich
;
Oliver Zett
;
Kristjan Axelsson
and
Maximilian Schumann
Affiliation:
Fraunhofer EMFT, Hansastr. 27d, Munich, Germany
Keyword(s):
First Responder, Automated Triage System, Mass-Casualty Incident, Blood Pressure Monitoring, AI, Machine Learning.
Abstract:
First responders often reach their limit when they have to find and triage dozens of victims in a mass-casualty incident. However, a delay in treatment directly affects the survival chances of seriously injured people. A method to reduce the time for the prehospital triage could potentially save lives. Hence, this work outlines the conceptual development of an intelligent bracelet that semi-automates the prehospital triage. This bracelet is supposed to enable non-professional first responders to help with the triage, which maximises the utilisation of man power at a mass-casualty incident. The bracelet should automate the part of the triage that is based on vital, position and movement data and it should guide through the necessary patient interactions. As a step towards this goal, this work proposes a semi-automated triage algorithm that is based on mSTaRT. One of the challenges to implement this concept is to measure the blood pressure with a small and easy to attach system. Theref
ore, this work presents a wrist worn oscillometric blood pressure measurement prototype. Furthermore, we discuss the use of machine learning methods to forecast triage level changes.
(More)