Objective
Mass-casualty incidents with injured persons caused by human-made or by natural disasters are increasing globally. In such situations, medical first responders (MFR) need to perform basic life support and first aid to help stabilize victims until arrival of further support. Proper evaluation of situations, checking and monitoring the vital states, and choosing the most appropriate strategy for proceeding with treatments are challenges. However, current training abilities for such scenarios are limited.
The MED1stMR consortium has identified Mixed Reality (MR) training as opportunity to better train and prepare MFRs for disasters. Thus, MED1stMR will develop a new generation of MR training providing haptic feedback through the integration of high-fidelity patient simulation manikins into MR. Thereby, MED1stMR offers a much richer sensory experience bringing MR training closer to reality. To enhance the effectiveness of MR training a physiological signal and trainee behavior feedback loop will be integrated for scenario control. In this respect, wearable technologies with body sensors will be developed allowing to monitor states and behaviour of MFR during training. Together with a model for effective performance in medical emergencies (EPME) this data will enable adapting training to trainee needs, manually or by artificial intelligence driven smart scenarios. Partnering MFR will be included in the project developments by an Agile End User Centred Research Methodology.
To this end, MED1stMR will pursue the following pioneering objectives: a) Developing a pioneering MR training approach for enhanced realism, b) Developing effective training scenarios and a training curriculum through user-centred design with cross-sectoral MFR, c) Realisation of a physiological signal and trainee behaviour feedback loop and EPME model for smart scenario control thereby enhancing effectiveness of MR training and d) To position the pioneering MR training approach across Europe
Fields of science
- natural sciencesearth and related environmental sciencesphysical geographynatural disaster
- natural sciencescomputer and information sciencesartificial intelligence
- natural sciencescomputer and information sciencesinternetinternet of things
- social sciencessociologygovernancecrisis management
- medical and health sciencesclinical medicineemergency medicine
Programme(s)
Call for proposal
H2020-SU-SEC-2020
See other projects for this call
Funding Scheme
RIA - Research and Innovation actionCoordinator
1210 Wien
Austria
Participants (18)
69117 Heidelberg
901 87 Umea
3012 Bern
8700 Leoben
8807 Freienbach
2630 369 Arruda Dos Vinhos
6836 BA Arnhem
41300 La Rinconada Sevilla
5310 Tiefgraben-mondsee
8010 Graz
8790 Eisenerz
54 248 Thessaloniki
1210 Wien
28020 Madrid
69120 Heidelberg
831 27 Ostersund
1000 Brussels
2520 Emblem Ranst