In 2019, the world’s fifteen most destructive events caused 4,500 victims.
Responding to these kinds of challenges, CyberEthics Lab. is partner in the ASSISTANCE project (Adapted Situation AwareneSS tools and taIlored training scenarios for increaSing capabiliTies and enhANcing the proteCtion of First RespondErs) studying how to improve the effectiveness of interventions by first responder organizations such as LEAs, firefighters, and emergency medical personnel, who play a key role in dealing with large natural and human-caused disasters.
ASSISTANCE is a project focused on enhancing the capacity of first responders to operate safely in widely varying disaster scenarios. In addition to identifying and defining new models of cooperation among different types of first responder organizations for the mitigation of major disasters, the project also enhances their capabilities to deal with complex situations related to different types of incidents through the use of technologies such as UAV3, smart sensors wearable by operators, swarms of robots and drones equipped with specific sensors to respond to the needs and contingencies of command of rescue operations. The project results will then be validated under controlled simulations of distructive events during three different pilots.
ASSISTANCE also aims to create an advanced European training network based on the use of virtual reality, mixed reality and augmented reality to empower first responders by taking into account the expectations and professional development needs of the operator/operator.
In this context, CyberEthics Lab. assesses the worthiness of innovative solutions designed to cushion the impact of disasters through ethical considerations and respect for the human rights of victims and rescue workers. Gender-based differences, such as considerations based on the situation of vulnerability typical of a disaster event, are examples of elements of analysis for efficient relief efforts under conditions of maximum safety while respecting people.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 832576.