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和死亡率的重要生物标志物，但对其进行纵向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期间被动测量心率和静息心率。我们的系统使用来自495名参与者的225,773个视频进行开发，并在实验室和自由生活条件下，使用来自205名参与者的185,970个视频进行了验证，代表了同类研究中规模最大的验证。与参考心电图相比，PHRM在浅、中、深三种肤色组的心率测量中，平均绝对百分比误差均低于10%；各肤色组间的MAPE无显著差异。PHRM测量的每日静息心率与可穿戴心率追踪器相比，平均绝对误差小于5 bpm，并且与已知风险因素相关。这些结果突显了智能手机实现被动且公平的心脏健康监测的潜力。