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arXiv · 2503.03783

Passive Heart Rate Monitoring During Smartphone Use in Everyday Life

Shun Liao·Paolo Di Achille·Jiang Wu·Silviu Borac·Jonathan Wang·Xin Liu·Eric Teasley·Lawrence Cai·Yuzhe Yang·Yun Liu·Daniel McDuff·Hao-Wei Su·Brent Winslow·Anupam Pathak·Shwetak Patel·James A. Taylor·Jameson K. Rogers·Ming-Zher Poh
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Abstract

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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Shun Liao, Paolo Di Achille, Jiang Wu, Silviu Borac, Jonathan Wang, Xin Liu, Eric Teasley, Lawrence Cai, Yuzhe Yang, Yun Liu, Daniel McDuff, Hao-Wei Su, Brent Winslow, Anupam Pathak, Shwetak Patel, James A. Taylor, Jameson K. Rogers, Ming-Zher Poh. 2025-03-04. Passive Heart Rate Monitoring During Smartphone Use in Everyday Life. https://arxiv.org/abs/2503.03783

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