Robust real-time chest compression rate detection from smartphone video

Globally one of our major mortality challenges is out-of-hospital cardiac arrest. Good quality cardiopulmonary resuscitation (CPR) is extremely important for the chance of survival after cardiac arrest. Research has shown that telephone assisted guidance from the dispatcher to the bystander can improve the CPR quality provided to the patient. Some recent work has proposed to use the accelerometer in a bystander's smartphone to estimate compression rates, but this demands the phone to be placed on the patient during compression. Our research group has previously proposed a real-time application for bystander and dispatcher feedback using the smartphone camera to estimate the chest compression rate while the smartphone is placed flat on the ground. Some shortcomings were observed with the application in high noise situations. In this paper we propose a robust method where we have modeled and parametrized the power specter density to distinguish between noisy situations, improved the update procedure for the dynamic region of interest and added post-processing steps to suppress noise. The proposed method provides excellent results with acceptable performance at 99.8% of the time testing different rates in high and low noise situations, 99.5% in a disturbance test, and 92.5% of the time during random movements.