Integrating Mobile and Cloud for PPG Signal Selection to Monitor Heart Rate during Intensive Physical Exercise

Abstract

Heart rate monitoring has become increasingly popular in the industry through mobile phones and wearable devices. However current determination of heart rate through mobile applications suffer from high corruption of signals during intensive physical exercise. In this paper, we present a novel technique for accurately determining heart rate during intensive motion by classifying PPG signals obtained from smartphones or wearable devices combined with motion data obtained from accelerometer sensors. Our approach utilizes the Internet of Things (IoT) cloud connectivity of smartphones for PPG signals selection using deep learning. The technique is validated using the TROIKA dataset and is accurately able to predict heart rate with a 10-fold cross validation error margin of 4.88%.