TeamAware is a Research & Innovation Action type project with a consortium of 24 private and public organisations from 13 different countries. An efficient and a robust management structure has been set to ensure that the TeamAware project delivers the targeted technical breakthroughs within the foreseen time and resource limits.
Visual scene analysis system analyses the emergency zone in terms of hazards and threats regarding buildings and infrastructure. It will be used to detect the victims and it will detect anomalies such as smoke, fire, abnormal heating, etc.
The images and videos captured by drone mounted cameras and head mounted cameras will be utilised for infrastructure monitoring system. It will be used to recognise and detect ruin and building damage identification; rubble identification; energy asset inspection; and building risk assessment.
TeamAware system deploys drone mounted electro optical, and acoustic event detection systems so that they can be used when it is hard or impossible to respond to an event due to obstacles or life-threatening risks.
Chemical detection system predicts the dispersion model of the chemical agents so that first responders can evacuate the zone. It is able to operate in joint and independent modes with chemical dispersion model and decision support.
Team monitoring system provides an integrated continuous outdoor/indoor localisation and health and body motion analysis system to monitor the health status, activities, motion anomalies and location of the first responders.
Citizen involvement and city integration system provides citizen involvement in events while using social media and city IoT sensor infrastructure.
TeamAware platform will integrate each system in terms of systems interoperability. Thus, standardisation and communication methods will be defined for data collection from heterogeneous sensors on a message communication bus. This protocol will be compatible with both 5G and ad-hoc sensor networks and adaptable to new type of networks, providing a flexibility for incorporating new types of sensor systems.
TeamAware platform will run as a service on cloud even if the infrastructure is collapsed. The data collected from various sensor systems will be analysed to monitor and manage surrounding events of the first responder as well as the activities of the first responders. The software will apply sensor fusion enhanced with artificial intelligence, specifically deep-learning algorithms, to correlate measurements and data coming from various sources.
TeamAware platform will present the “Common Situational Awareness Picture” via operation centre to the operators in the central office as well as the responders in the field. In detail, the intensive information will be refined and filtered to obtain a clear and manageable information presented on the user interface. The displays will utilise AR/ mobile interface to obtain a comprehensible and standard (ISO 9241) user interface.
There will be two demonstrations, namely “natural disaster” and “human-made disaster” in real environment so that participating end users will deploy, test and measure the performance under real conditions with direct participation of the end users.
In TeamAware project, a total of 7 end-user organizations (2 of them are fire fighters, 3 of them are medical emergency organisations, 1 of them is LEA, and 1 of them is first responder network organisation) participating as full partners in the consortium. Read more
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101019808.
Project Acronym
TeamAware
Project Name
Team Awareness Enhanced with Artificial Intelligence and Augmented Reality
Topic
SU-DRS02-2018-2019-2020
Budget
6.964.702 EUR
Duration
May 2021 - May 2024
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Contact
Monica Florea
Administrative Coordinator
European Projects Department
SIMAVI
Soseaua Bucuresti-Ploiesti 73-81 COM
Bucuresti/ROMANIA
Email:
Çağlar Akman
Technical Coordinator
Command and Control Systems
HAVELSAN
Eskişehir Yolu 7 km
Ankara/TURKEY
Email:
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101019808.