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Bibliographic Details
Main Authors:	Liao, Shun, Di Achille, Paolo, Wu, Jiang, Borac, Silviu, Wang, Jonathan, Liu, Xin, Teasley, Eric, Cai, Lawrence, Yang, Yuzhe, Liu, Yun, McDuff, Daniel, Su, Hao-Wei, Winslow, Brent, Pathak, Anupam, Patel, Shwetak, Taylor, James A., Rogers, Jameson K., Poh, Ming-Zher
Format:	Preprint
Published:	2025
Subjects:	Tissues and Organs Artificial Intelligence Emerging Technologies Human-Computer Interaction Machine Learning
Online Access:	https://arxiv.org/abs/2503.03783
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Table of Contents:

Resting heart rate (RHR) is an important biomarker of cardiovascular health and mortality, but tracking it longitudinally generally requires a wearable device, limiting its availability. We present PHRM, a deep learning system for passive heart rate (HR) and RHR measurements during everyday smartphone use, using facial video-based photoplethysmography. Our system was developed using 225,773 videos from 495 participants and validated on 185,970 videos from 205 participants in laboratory and free-living conditions, representing the largest validation study of its kind. Compared to reference electrocardiogram, PHRM achieved a mean absolute percentage error (MAPE) < 10% for HR measurements across three skin tone groups of light, medium and dark pigmentation; MAPE for each skin tone group was non-inferior versus the others. Daily RHR measured by PHRM had a mean absolute error < 5 bpm compared to a wearable HR tracker, and was associated with known risk factors. These results highlight the potential of smartphones to enable passive and equitable heart health monitoring.

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