用手机摄像头无感测心率，适合日常长期监测
Passive Heart Rate Monitoring During Smartphone Use in Everyday Life
- 通过手机面部视频做光体积描记，实现无接触心率测量
- 心率误差低于10%，不同肤色人群表现一致
- 日均静息心率误差小于5次/分钟，适合健康追踪
静息心率（RHR）是心血管健康与死亡率的重要生物标志物，但长期监测通常依赖可穿戴设备，使用受限。本文提出PHRM，一种基于面部视频光电容积脉搏波描记术的深度学习系统，可在日常手机使用中被动测量心率与静息心率。系统基于495名参与者共225,773段视频训练，验证数据来自205名参与者共185,970段视频，涵盖实验室与自由生活条件，为同类研究中规模最大的验证。与参考心电图相比，PHRM在轻、中、深肤色三组中均实现心率测量平均绝对百分比误差（MAPE）<10%，各组间无显著差异；每日静息心率与可穿戴设备对比，平均绝对误差<5 bpm，且与已知风险因素相关。结果表明智能手机具备实现无感、公平心率监测的潜力。
原文摘要 · Abstract (English)
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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