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

TL;DR

This paper introduces an evolutionary algorithm-based method to accurately estimate CPR quality parameters, such as compression frequency and depth, using wrist-worn inertial sensors, enabling real-time feedback via smartwatches.

Contribution

It presents a novel sinusoidal model-fitting approach based on an Evolution Strategy for CPR parameter estimation from wrist sensors, suitable for continuous support systems.

Findings

Variance of ±2.22 cpm in compression frequency estimation

Wrist-worn IMUs are viable for CPR quality monitoring

Method enables integration into smartphone or smartwatch apps

Abstract

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.