An evolutionary approach to continuously estimate CPR quality parameters from a wrist-worn inertial sensor
This enables bystanders to improve CPR through continuous feedback via smartwatch apps, though it appears incremental as it adapts existing methods to a new sensor position.
The paper tackles the problem of estimating CPR quality parameters (chest compression frequency and depth) from a wrist-worn inertial sensor, achieving a variance of ±2.22 compressions per minute for frequency estimation compared to a training mannequin reference.
Cardiopulmonary resuscitation (CPR) is one of the most critical emergency interventions for sudden cardiac arrest. In this paper, a robust sinusoidal model-fitting method based on a Evolution Strategy inspired algorithm for CPR quality parameters -- naming chest compression frequency and depth -- as measured by an inertial measurement unit (IMU) attached to the wrist is presented. The proposed approach will allow bystanders to improve CPR as part of a continuous closed-loop support system once integrated into a smartphone or smartwatch application. By evaluating the model's precision with data recorded by a training mannequin as reference standard, a variance for the compression frequency of $\pm 2.22$ compressions per minute (cpm) has been found for the IMU attached to the wrist. It was found that this previously unconsidered position and thus, the use of smartwatches is a suitable alternative to the typical placement of phones in hand for CPR training.