Cardiopulmonary resuscitation (CPR) is a first-aid key survival technique used to stimulate breathing and keep blood flowing to the heart. Its effective administration can significantly increase the chances of survival in victims of cardiac arrest. In this paper, we propose a markerless system for quality CPR training based on RGB-D (RGB + Depth) sensors, called RELIVE. Then, we report the results of a series of experimental tests conducted to evaluate RELIVE tracking performance. The proposed system is able to accurately track the 3-D position of the hands performing CPR by means of RGB-D sensors to estimate the chest compression rate and depth, providing a real-time visual/audio feedback about the rescuer's performance. Finally, the system usability has been assessed by both healthcare professionals and lay people.

RELIVE: A Markerless Assistant for CPR Training

LOCONSOLE, Claudio;
2016-01-01

Abstract

Cardiopulmonary resuscitation (CPR) is a first-aid key survival technique used to stimulate breathing and keep blood flowing to the heart. Its effective administration can significantly increase the chances of survival in victims of cardiac arrest. In this paper, we propose a markerless system for quality CPR training based on RGB-D (RGB + Depth) sensors, called RELIVE. Then, we report the results of a series of experimental tests conducted to evaluate RELIVE tracking performance. The proposed system is able to accurately track the 3-D position of the hands performing CPR by means of RGB-D sensors to estimate the chest compression rate and depth, providing a real-time visual/audio feedback about the rescuer's performance. Finally, the system usability has been assessed by both healthcare professionals and lay people.
2016
3-D movement tracking
Cardiopulmonary resuscitation (CPR) human performance assessment
computer vision
training
Human Factors and Ergonomics
Control and Systems Engineering
Signal Processing
Human-Computer Interaction
Computer Science Applications1707 Computer Vision and Pattern Recognition
Computer Networks and Communications
Artificial Intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12606/1922
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