Motion artifact reduction in photoplethysmography using Bayesian classification for physical exercise identification

Accurate heart rate (HR) estimation from photoplethysmography (PPG) recorded from subjects' wrist when the subjects are performing various physical exercises is a challenging problem. This paper presents a framework that combines a robust algorithm capable of estimating HR from PPG signal with subjects performing a single exercise and a physical exercise identification algorithm capable of recognizing the exercise the subject is performing. Experimental results on subjects performing two different exercises show that an improvement of about 50% in the accuracy of HR estimation is achieved with the proposed approach.