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Cardiopulmonary resuscitation (CPR) is one of the most critical emergencyinterventions for sudden cardiac arrest. In this paper, a robust sinusoidalmodel-fitting method based on a Evolution Strategy inspired algorithm for CPRquality parameters -- naming chest compression frequency and depth -- asmeasured by an inertial measurement unit (IMU) attached to the wrist ispresented. The proposed approach will allow bystanders to improve CPR as partof a continuous closed-loop support system once integrated into a smartphone orsmartwatch application. By evaluating the model's precision with data recordedby a training mannequin as reference standard, a variance for the compressionfrequency of $\pm 2.22$ compressions per minute (cpm) has been found for theIMU attached to the wrist. It was found that this previously unconsideredposition and thus, the use of smartwatches is a suitable alternative to thetypical placement of phones in hand for CPR training.

An evolutionary approach to continuously estimate CPR quality parameters from a wrist-worn inertial sensor