Nouhaila Fraihi, Ouassim Karrakchou, Mounir Ghogho
5 min
Reliable heart rate (HR) and respiratory rate (RR) estimation from wrist-worn photoplethysmography (PPG) is frequently compromised by motion artifacts that overlap with physiological signals. Existing deep learning methods often treat this as a black-box regression problem, which can lead to reliance on spurious correlations rather than true physiological dynamics. This paper asks whether explicitly modeling the physics of PPG signal generation can improve robustness and interpretability in free-living conditions.
The authors propose the Physically-Constrained Harmonic Separation (PCHS) framework. Instead of direct regression, PCHS treats signal processing as an analysis-by-synthesis problem. It uses a dual-stream encoder where accelerometer data conditions the PPG representation via feature-wise linear modulation (FiLM). A physics-informed decoder then synthesizes a pulse-synchronous physiological signal using a harmonic model, while an additive residual branch captures non-harmonic motion artifacts. The model also predicts a time-varying uncertainty estimate to downweight segments where motion corruption is too severe for reliable inference.
PCHS outperforms state-of-the-art signal processing and deep learning methods on the motion-intensive PPG-DaLiA dataset, achieving a mean absolute error of 3.20 bpm for heart rate. The framework provides interpretable signal decompositions, effectively isolating cardiac structure from motion-induced noise. Ablation studies confirm that the combination of harmonic synthesis, motion-aware conditioning, and uncertainty-based inference is essential for maintaining accuracy under high-motion conditions.
By moving away from black-box regression toward physiology-informed generative modeling, this approach provides a more robust way to handle the non-stationary motion artifacts inherent in wearable sensing. The interpretability of the decomposed signals allows for better validation of the model's internal logic, making it a promising step toward reliable, continuous health monitoring in daily life.
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.
Alex: What does the learned loss weighting tell us about where the hard problem actually sits?
Sam: The respiratory rate term ends up weighted substantially higher than the separation loss, which is informative. Respiratory rate is extracted from the low-frequency envelope modulations of the PPG — a much weaker signal than the pulse itself. By weighting it higher through homoscedastic uncertainty, the model is effectively allocating more optimization pressure to the harder sub-task. The separation loss then acts as a regularizer, keeping the motion residual distinct without dominating. It's a principled way to balance competing objectives without manual hyperparameter search.
Alex: That's a clean design. But the quasi-periodicity assumption feels like a meaningful constraint — how far does it hold?
Sam: For a healthy user in free-living conditions, the pulse is regular enough that the assumption holds well. But take it into a clinical setting with atrial fibrillation, and it breaks down. The cardiac rhythm becomes irregular, the harmonic model loses its anchor, and you'd expect the residual branch to absorb signal it shouldn't. The authors acknowledge this — it's the clearest boundary on where the framework applies.
Alex: So the practical scope is consumer wearables and research-grade monitoring in healthy populations, not clinical arrhythmia detection.
Sam: That's the honest read. The framework is a meaningful advance for disentangling motion artifact from physiology in daily life — interpretable, physically grounded, and with an uncertainty mechanism that degrades gracefully rather than failing silently. Extending it to non-periodic cardiac dynamics is the obvious next problem. But on the terrain it's designed for, the design choices are well-motivated and the ablations support the core claims.
Alex: A framework that knows when it doesn't know — that's a useful property in any monitoring system. Thanks for walking us through it, Sam, and thanks to our listeners for joining us on ResearchPod.