A modified Genetic Algorithm for continuous estimation of CPR quality parameters from wrist-worn inertial sensor data
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Cardiopulmonary resuscitation (CPR) is the most important emergency intervention for sudden cardiac arrest. In this paper, a robust sinusoidal model fitting method based on a modified Genetic Algorithm for CPR quality parameters - naming chest compression frequency and depth - as measured by an inertial sensor placed at the wrist is presented. Once included into a smartphone or smartwatch app, the proposed algorithm will enable bystanders to improve CPR (as part of a continuous closed-loop support-system). By evaluating the precision of the model with both, simulated data and data recorded by a Laerdal Resusci Anne mannequin as reference standard, a variance for compression frequency of +-3.7 cpm has been found for the sensor placed at the wrist. Thereby, this previously unconsidered position and consequently the use of smartwatches was shown to be a suitable alternative to the typical placement of phones in the hand for CPR training.
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