Robust PPG-based Ambulatory Heart Rate Tracking Algorithm

Abstract

Recent advances in wearable devices with optical Photoplethysmography (PPG) and actigraphy have enabled inexpensive, accessible, and convenient Heart Rate (HR) monitoring. Nevertheless, PPG's susceptibility to motion presents challenges in obtaining reliable and accurate HR estimates during ambulatory and intense activity conditions. This study proposes a lightweight HR algorithm, TAPIR: a Time-domain based method involving Adaptive filtering, Peak detection, Interval tracking, and Refinement, using simultaneously acquired PPG and accelerometer signals. The proposed method is applied to four unique, wrist-wearable based, publicly available databases that capture a variety of controlled and uncontrolled daily life activities, stress, and emotion. The results suggest that the current HR prediction is significantly (P<0.01) more accurate during intense activity conditions than the contemporary algorithms involving Wiener filtering, time-frequency analysis, and deep learning. The current HR tracking algorithm is validated to be of clinical-grade and suitable for low-power embedded wearable systems as a powerful tool for continuous HR monitoring in real-world ambulatory conditions.