EA-based smartwatch application for training and assistance in cardiopulmonary resuscitation

Abstract

Cardiopulmonary resuscitation (CPR) is critical to the prevention of death from cardiac arrest. Smartwatches can help first responders perform CPR correctly by collecting real-time acceleration data to determine compression rate and depth. We present an approach for a smartwatch application that uses motion data from the inertial sensors and an evolutionary algorithm ((μ + λ)-ES) to compute compression depth and frequency and provide corrective feedback. The application was optimized for out-of-hospital CPR and evaluated in a pilot study using a CPR training manikin as a reference system and an existing smartphone app for comparison.