Physically-Constrained Harmonic Separation for Robust Heart and Respiratory Rate Estimation from Wrist Photoplethysmography
arXiv:2606.30156v1 Announce Type: cross 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) frame
The increasing availability of wearable sensors and the maturation of AI/ML techniques for signal processing are enabling more robust physiological monitoring in real-world conditions.
Improved, reliable health monitoring from everyday wearables can provide continuous, non-invasive insights into cardiopulmonary health, impacting preventative care and chronic disease management.
The ability to accurately estimate heart and respiratory rates from noisy wrist PPG data, even with motion, significantly enhances the utility and reliability of consumer and clinical wearables.
- · Wearable device manufacturers
- · Health tech companies
- · Preventative medicine
- · Patients with chronic conditions
- · Traditional clinical monitoring equipment (for some use cases)
- · Less robust signal processing methods
More accurate and pervasive health data collection from everyday activities.
Earlier detection of health issues and more personalized health interventions based on continuous physiological trends.
Reduced healthcare costs through preventative measures and a shift towards proactive, rather than reactive, health management.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG