Abstract Background Pediatric CPR quality remains inconsistent, with low adherence to guideline-recommended compression metrics. AR-CPR is an augmented reality feedback system designed to improve CPR performance using real-time, in-view coaching via smart glasses. Aim To validate the accuracy and precision of the AR-CPR system in measuring chest compression rate and depth across clinically relevant ranges. Methods We tested AR-CPR using an oscillation platform at five rates and four depths, and with the Stryker LUCAS3 device at 102 CPM and 5.3 cm. A total of 473 compressions (slide test) and 559 compressions (LUCAS3 test) were analyzed. Statistical methods included intraclass correlation coefficients, paired t-tests, Bland-Altman analysis, root mean square error (RMSE), kernel density estimation for error distribution, and group error modeling to estimate clinical thresholds. Linearity was assessed via linear regression. Results The AR-CPR system demonstrated high accuracy and reliability in 473 simulated and 559 mechanical compressions. Mean biases were minimal for rate (-0.48, -0.44 CPM) and depth (+0.39, +0.59 cm), with excellent ICCs (0.997 rate, 0.944 depth). Errors were normally distributed, with 7% exceeding clinically relevant thresholds. R2 values (0.994 rate, 0.903 depth) confirmed strong linear agreement with reference values. Conclusion AR-CPR reliably measured compression rate and depth with high accuracy and precision across variable and fixed testing conditions. Its portability, real-time feedback, and robust signal processing support its potential for improving pediatric resuscitation training and clinical performance.
Kleinman et al. (Sat,) studied this question.
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