CARMA: A Robust Motion Artifact Reduction Algorithm for Heart Rate Monitoring from PPG Signals

Photoplethysmography (PPG) is a non invasive measurement of the blood flow, that can be used instead of electrocardiography to estimate heart rate (HR). Most existing techniques used for HR monitoring in fitness with PPG focus on slowly running alone, while those suitable for intensive physical exercise need an initialization stage in which wearers are required to stand still for several seconds. This paper present a novel algorithm for HR estimation from PPG signal based on motion artifact removal (MAR) and adaptive tracking (AT) that overcomes limitations of the previous techniques. Experimental evaluations performed on datasets recorded from several subjects during running show an average absolute error of HR estimation of 2.26 beats per minute, demonstrating the validity of the presented technique to monitor HR using wearable devices which use PPG signals.