TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise

TL;DR

TROIKA is a comprehensive framework that accurately estimates heart rate from wrist PPG signals during intense exercise by effectively removing motion artifacts through advanced signal processing techniques.

Contribution

The paper introduces TROIKA, a novel general framework combining denoising, high-resolution spectrum estimation, and peak tracking for robust heart rate monitoring during vigorous activity.

Findings

Average absolute error of 2.34 BPM in heart rate estimation

Pearson correlation of 0.992 with ground-truth heart rate

Robust performance against strong motion artifacts

Abstract

Heart rate monitoring using wrist-type photoplethysmographic (PPG) signals during subjects' intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. So far few works have studied this problem. In this work, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/hour showed that the average absolute error of heart rate estimation was 2.34 beat per minute (BPM), and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.992. This framework is of great values to wearable devices such as smart-watches which use PPG signals to monitor heart rate for fitness.