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.

翻译：静息心率是心血管健康与死亡率的重要生物标志物，但对其进行长期追踪通常需要可穿戴设备，这限制了其可及性。我们提出了PHRM，一种基于面部视频光电容积描记法的深度学习系统，用于在日常智能手机使用过程中被动测量心率与静息心率。该系统基于495名参与者的225,773段视频数据开发，并在实验室与自由生活环境下，通过205名参与者的185,970段视频数据进行了验证，代表了该领域迄今规模最大的验证研究。与参考心电图相比，PHRM在浅、中、深三种肤色组的心率测量中均实现了平均绝对百分比误差低于10%；各肤色组间的误差表现无显著差异。与可穿戴心率追踪设备相比，PHRM测得的日平均静息心率其平均绝对误差小于5次/分钟，且与已知风险因素存在关联。这些结果凸显了智能手机在实现被动、公平的心血管健康监测方面的潜力。