Passive Heart Rate Monitoring During Smartphone Use in Everyday Life

Shun Liao, Paolo Di Achille, Jiang Wu, Silviu Borac, Jonathan Wang +13 more
3/4/2025
q-bio.TOcs.AIcs.ET

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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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Cite this paper

@article{liao2025passive,
  title  = {Passive Heart Rate Monitoring During Smartphone Use in Everyday Life},
  author = {Shun Liao and Paolo Di Achille and Jiang Wu and Silviu Borac and Jonathan Wang and Xin Liu and Eric Teasley and Lawrence Cai and Yuzhe Yang and Yun Liu and Daniel McDuff and Hao-Wei Su and Brent Winslow and Anupam Pathak and Shwetak Patel and James A. Taylor and Jameson K. Rogers and Ming-Zher Poh},
  year   = {2025},
  eprint = {2503.03783},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.03783v3}
}

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