Physically-Constrained Harmonic Separation for Robust Heart and Respiratory Rate Estimation from Wrist Photoplethysmography
2026-06-29 • Artificial Intelligence
Artificial IntelligenceMachine Learning
AI summaryⓘ
The authors developed a new method called Physically-Constrained Harmonic Separation (PCHS) to better estimate heart rate and breathing rate from wrist sensor data. Unlike previous methods, their approach uses a physics-based model that separates true body signals from motion noise using accelerometer data. This helps make the results more reliable during everyday movements and also easier to understand. Their tests showed that PCHS performed better than existing methods on a challenging dataset with lots of motion.
photoplethysmography (PPG)heart rate (HR)respiratory rate (RR)motion artifactsaccelerometerharmonic decompositionsignal processinganalysis-by-synthesisphysiological monitoringPCHS
Authors
Nouhaila Fraihi, Ouassim Karrakchou, Mounir Ghogho
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.