This work illustrates the testing of the Spot Robot performed at the training camp of Civil Protection and ANPAS (National Association of Public Assistance) in Foligno. The camp simulates the aftermath of an earthquake with different types of collapsed buildings. We teleoperated the quadruped Spot robot in different areas of the camp where Spot needs to address different challenges. The focus of the testing was not on the objective performance of the robot but on how the robot was subjectively perceived by rescuers of ANPAS and Civil Protection. Initially, we formulated and tested two hypotheses to check if locomotion in some areas is perceived better than in other areas and if there are perceivable differences when the robot is using different types of locomotion gaits. Then, we conducted unstructured interviews with participants who observed the robot in action to describe their rescue procedures and give us suggestions and opinions on what operations they expect the robot might perform.

In this work, we propose a strategy to implement the first two steps of the DRABC paradigm (Danger, Response, Airway, Breathing, Circulation) used by rescuers in Search and Rescue (SAR) with the use of a mobile quadruped robot. The robot is programmed to autonomously explore and create a map of the environment with the main objective of identifying areas of danger and reporting them to rescuers (first step of DRABC). While completing this first goal the robot must also identify people still inside the building, mark their position but also evaluate the health state of the person and in particular the response (second step of DRABC). Specifically, we propose new strategies for SAR considering that autonomous behaviour is particularly relevant before the human rescuers arrive: therefore, the policy adopted should privilege covering a broader area in the available time, rather than exploring a smaller area in depth. Strategies have been tested with the Spot robot from Boston Dynamics concerning both exploration and health assessment. The software developed and the tests to validate it are thoroughly described and explained.

This paper proposes a real-time system for pose estimation of an Unmanned Aerial Vehicle (UAV) using parallel image processing of a known marker. The system exploits the capabilities of a high-performance CPU/GPU embedded system in order to provide on-board high-frequency pose estimation, eliminating the need for transmitting the video stream off-board, and enabling autonomous takeoff and landing. The system is evaluated extensively with lab and field tests on board a small quadrotor. The results show that the proposed system is able to provide precise pose estimation with a framerate of at least 30 fps and an image resolution of 640×480 pixels. The use of the GPU for image filtering and marker detection provides an upper bound on the required computation time regardless of the complexity of the image thereby allowing for robust marker detection even in cluttered environments.

The increasing diffusion of chronic diseases represents a challenge for the health systems that have to deal with rising costs caused by the large number of patients. The ICT-enabled telemedicine systems represent a promising solution to provide effective treatments, to reduce hospitalizations and related costs and to improve the patients' quality of life. These systems allow to monitor the clinical status of the patients through self-measured vital signs. The electrocardiogram (ECG) represents one of the most practiced clinical exams in the treatment of chronic patients, but ECG devices currently known for medical purpose are difficult to be used by non-skilled persons, and they are definitively not suitable for such telemedicine systems. This paper introduces a user-friendly and ergonomic device conceived to be used autonomously by the patient to record high quality ECGs. The device acquires the first lead of the ECG from the patient's hands exploiting dry metal electrodes. The device requires few basic actions to be operated and can transmit collected data via Bluetooth connectivity. The developed dedicated API makes also possible an easy integration of the device into telemedicine systems.