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

TLDR

Heart rate monitoring using wrist‑type photoplethysmographic signals during intensive exercise is difficult due to strong motion artifacts from hand movements, and few studies have addressed this problem. The study proposes TROIKA, a general framework for heart rate monitoring during intense exercise. TROIKA denoises PPG signals via decomposition, estimates high‑resolution spectra with sparse reconstruction, tracks spectral peaks with verification, and allows straightforward derivation of variants. Experimental results on 12 subjects running at 15 km/h show TROIKA achieves an average absolute error of 2.34 bpm, a Pearson correlation of 0.992 with ground truth, and demonstrates robustness to motion artifacts, making it valuable for wearable devices.

Abstract

Heart rate monitoring using wrist-type photoplethysmographic 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 study, 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/h showed that the average absolute error of heart rate estimation was 2.34 beat per minute, 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 smartwatches which use PPG signals to monitor heart rate for fitness.