A modified Genetic Algorithm for continuous estimation of CPR quality parameters from wrist-worn inertial sensor data

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.

Cardiopulmonary Resuscitation Quality Parameters from Inertial Sensor Data using Differential Evolution Fitting of Sinusoids

In this paper, we present a robust sinusoidal model fitting method based on the Differential Evolution (DE) algorithm for determining cardiopulmonary resuscitation (CPR) quality-parameters - naming chest compression frequency and depth - as measured by an inertial sensor placed at the wrist. Once included into a smartphone or smartwatch app, our proposed algorithm will enable laypersons to improve cardiopulmonary resuscitation (as part of a continuous closed-loop support-system). By evaluating the sensitivity of the model with data recorded by a Laerdal Resusci Anne mannequin as reference standard, a low variance for compression frequency of +-2.7 cpm (2.5 %) has been found for the sensor placed at the wrist, making this previously not evaluated position a suitable alternative to the typical smartphone placement in the hand.

Cardiopulmonary Resuscitation Quality Parameters from Motion Capture Data using Differential Evolution Fitting of Sinusoids

Cardiopulmonary resuscitation (CPR) is alongside with electrical defibrillation the most important treatment for sudden cardiac arrest, which affects thousands of individuals every year. In this paper, we present a robust sinusoid model that uses skeletal motion data from an RGB-D (Kinect) sensor and the Differential Evolution (DE) optimization algorithm to dynamically fit sinusoidal curves to derive frequency and depth parameters for cardiopulmonary resuscitation training. It is intended to be part of a robust and easy-to-use feedback system for CPR training, allowing its use for unsupervised training. The accuracy of this DE-based approach is evaluated in comparison with data recorded by a state-of-the-art training mannequin. We optimized the DE algorithm constants and have shown that with these optimized parameters the frequency of the CPR is recognized with a median error of 2.55 (2.4%) compressions per minute compared to the reference training mannequin.