A novel depth estimation algorithm of chest compression for feedback of high-quality cardiopulmonary resuscitation based on a smartwatch

Highlights

• An algorithm developed for a smartwatch app to detect chest compression depth.

• The algorithm was based on the watch’s maximal acceleration during compressions.

• A validation study showed no difference between our app and a reference standard.

Abstract

Introduction

High-quality cardiopulmonary resuscitation (CPR) is a key factor affecting cardiac arrest survival. Accurate monitoring and real-time feedback are emphasized to improve CPR quality. The purpose of this study was to develop and validate a novel depth estimation algorithm based on a smartwatch equipped with a built-in accelerometer for feedback instructions during CPR.

Methods

For data collection and model building, researchers wore an Android Wear smartwatch and performed chest compression-only CPR on a Resusci Anne QCPR training manikin. We developed an algorithm based on the assumptions that (1) maximal acceleration measured by the smartwatch accelerometer and the chest compression depth (CCD) are positively correlated and (2) the magnitude of acceleration at a specific time point and interval is correlated with its neighboring points. We defined a statistic value M as a function of time and the magnitude of maximal acceleration. We labeled and processed collected data and determined the relationship between M value, compression rate and CCD. We built a model accordingly, and developed a smartwatch app capable of detecting CCD. For validation, researchers wore a smartwatch with the preinstalled app and performed chest compression-only CPR on the manikin at target sessions. We compared the CCD results given by the smartwatch and the reference using the Wilcoxon Signed Rank Test (WSRT), and used Bland-Altman (BA) analysis to assess the agreement between the two methods.

Results

We analyzed a total of 3978 compressions that covered the target rate of 80–140/min and CCD of 4–7 cm. WSRT showed that there was no significant difference between the two methods (P = 0.084). By BA analysis the mean of differences was 0.003 and the bias between the two methods was not significant (95% CI: −0.079 to 0.085).

Conclusion

Our study indicates that the algorithm developed for estimating CCD based on a smartwatch with a built-in accelerometer is promising. Further studies will be conducted to evaluate its application for CPR training and clinical practice.