Johns Hopkins University
This study evaluates whether an augmented reality cardiopulmonary resuscitation feedback system (AR-CPR) can support health care providers in delivering high-quality chest compressions during pediatric cardiac arrest. AR-CPR provides real-time visual feedback on chest compression rate, depth, and recoil through a head-mounted augmented reality display. In this randomized, multicenter, international, simulation-based non-inferiority study, health care providers perform chest compressions during an 18-minute simulated pediatric cardiac arrest. Participants are assigned to receive either real-time feedback from AR-CPR or coaching from a trained human CPR coach. The primary objective is to determine whether the percentage of chest compressions meeting guideline targets for both rate and depth with AR-CPR is non-inferior to that achieved with human CPR coaching.
This prospective, multicenter, international, randomized, unblinded, non-inferiority study evaluates an augmented reality cardiopulmonary resuscitation feedback system (AR-CPR) compared with traditional human-delivered quality CPR coaching during simulated pediatric cardiac arrest. The study uses a standardized 18-minute pediatric cardiopulmonary arrest simulation conducted across eight participating institutions in the United States and Canada. Participants are actively credentialed pediatric emergency department or intensive care unit nurses and clinical technicians with current Pediatric A…
Inclusion Criteria: * Actively credentialed pediatric emergency department or pediatric intensive care unit nurse or clinical technician at a participating study site * Current American Heart Association (AHA) Pediatric Advanced Life Support (PALS) certification Exclusion Criteria: * Inability to physically perform chest compressions * Need for prescription corrective lenses without having those corrective lenses available at the time of study participation
AR-CPR is a real-time augmented reality CPR feedback system designed to guide chest compression performance during pediatric cardiac arrest. The system uses a compression sensor containing an inertial measurement unit (IMU) and force-sensitive resistor (FSR) connected to a microcontroller. Sensor data are relayed to a Raspberry Pi 5 microcomputer running custom AR-CPR Coach software, which calculates chest compression rate, depth, and recoil. These metrics are transmitted wirelessly to a Vuzix M400 head-mounted display. The augmented reality interface provides numerical values and visual cues within the user's field of view. When performance is outside established targets, the system provides qualitative feedback and, for persistent deviations, escalates to explicit goal-directed instructions.