Heart Rate Tracking using Wrist-Type Photoplethysmographic (PPG) Signals during Physical Exercise with Simultaneous Accelerometry

• Published in: IEEE signal processing letters Vol. 23; no. 2; pp. 227 - 231
• Main Authors: Boloursaz Mashhadi, Mahdi, Asadi, Ehsan, Eskandari, Mohsen, Kiani, Shahrzad, Marvasti, Farokh
• Format: Journal Article
• Language: English
• Published: IEEE 01.02.2016
• Subjects: Acceleration; Adaptive filters; Adaptive motion artifact cancellation; Algorithm design and analysis; causal heart rate monitoring; Heart rate; iterative method with adaptive thresholding (IMAT); photoplethysmograph (PPG); Signal processing algorithms; singular value decomposition (SVD); sparse spectrum estimation; Spectral analysis; Time-frequency analysis
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2015.2509868

This letter considers the problem of casual heart rate tracking during intensive physical exercise using simultaneous 2 channel photoplethysmographic (PPG) and 3 dimensional (3D) acceleration signals recorded from wrist. This is a challenging problem because the PPG signals recorded from wrist during exercise are contaminated by strong Motion Artifacts (MAs). In this work, a novel algorithm is proposed which consists of two main steps of MA Cancellation and Spectral Analysis. The MA cancellation step cleanses the MA-contaminated PPG signals utilizing the acceleration data and the spectral analysis step estimates a higher resolution spectrum of the signal and selects the spectral peaks corresponding to HR. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/hour showed that the proposed algorithm achieves an average absolute error of 1.25 beat per minute (BPM). These experimental results also confirm that the proposed algorithm keeps high estimation accuracies even in strong MA conditions.