Physically-Constrained Harmonic Separation for Robust Heart and Respiratory Rate Estimation from Wrist Photoplethysmography

2026-06-29

AI Summary

This study addresses the challenge of motion artifacts corrupting wrist-based photoplethysmography (PPG) signals during daily activities, which degrades the accuracy of heart rate and respiratory rate estimation. The authors propose an accelerometer-guided, physics-constrained harmonic separation framework that models physiological signals through an analysis–synthesis process. By leveraging a physically interpretable harmonic generation mechanism, the method decouples cardiac and respiratory modulation components, while incorporating uncertainty-aware weighting to optimize signal reconstruction and enhance robustness to motion. Evaluated on the PPG-DaLiA dataset, the approach significantly outperforms existing methods, achieving highly accurate and interpretable joint estimation of heart rate and respiratory rate, and effectively separating physiological signals from motion-induced artifacts.

Abstract

Wrist-worn photoplethysmography (PPG) enables continuous monitoring of cardiopulmonary physiology, but reliable heart rate (HR) and respiratory rate (RR) estimation in free-living conditions remains challenging due to non-stationary motion artifacts that spectrally overlap with physiological dynamics. Existing signal-processing methods degrade under strong motion, while unconstrained deep learning approaches often lack physiological interpretability and identifiable structure. We propose a Physically-Constrained Harmonic Separation (PCHS) framework that formulates HR and RR estimation from wrist PPG as an analysis-by-synthesis problem, where accelerometer measurements condition artifact separation rather than directly regressing vital signs. A physics-guided harmonic generator decomposes the observed signal into quasi-periodic physiological components and a motion-related residual, enabling HR recovery from the fundamental frequency and RR prediction from respiratory-driven modulations of the harmonic parameters. Robust reconstruction objectives, separation constraints, and uncertainty-aware weighting stabilize the decomposition under motion. Experiments on the motion-intensive PPG-DaLiA dataset demonstrate that PCHS outperforms state-of-the-art methods while yielding interpretable signal decompositions that effectively disentangle physiological activity from motion artifacts.

Problem

• photoplethysmography
• heart rate estimation
• respiratory rate estimation
• motion artifacts
• physiological signal separation

Innovation

• Physically-Constrained Harmonic Separation
• analysis-by-synthesis
• motion artifact separation
• physiological interpretability
• wrist PPG