Passive Heart Rate Monitoring During Smartphone Use in Everyday Life
This enables passive and equitable heart health monitoring for smartphone users, offering a scalable alternative to wearables.
The researchers tackled the problem of tracking resting heart rate without wearables by developing a deep learning system that uses facial videos from smartphones to measure heart rate, achieving a mean absolute percentage error under 10% across different skin tones and a mean absolute error under 5 bpm for daily resting heart rate compared to wearables.
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.