StreamPPG实现毫秒级心率监测，边端设备实时运行
StreamPPG: Low-Latency rPPG Estimation via Consistent Privileged Learning
- 帧级输入+一致性特权学习，突破传统延迟瓶颈
- 在多个数据集上达到顶尖精度，延迟低于100毫秒
- 适合医疗可穿戴、智能终端等实时健康场景
远程光电容积脉搏波图（rPPG）通过面部视频估计血容量脉冲（BVP）信号，实现无接触健康监测。传统片段式方法需采集超过一百帧才能推理，引入数秒延迟，难以实现实时应用；而帧级方法因难以捕捉生理节律的长程时序特征，导致估计精度下降。为此，我们提出StreamPPG，一种统一架构，可在保持帧级输入的同时实现低延迟生理信号估计，并达到与片段式方法相当的精度。该模型采用一致性特权学习（CPL）策略，利用真实rPPG信号作为特权信息提升表征能力。大量实验表明，StreamPPG在多个数据集上均达到最先进性能，且可在边缘设备上实现实时吞吐。
原文摘要 · Abstract (English)
Remote photoplethysmography (rPPG) estimates the blood volume pulse (BVP) signal from facial videos, enabling contact-free health monitoring. Conventional clip-wise approaches, which use video clips as input, require capturing over one hundred frames before inference, thus introducing several seconds of delay and hindering real-time use. Meanwhile, frame-wise approaches struggle to capture long-range temporal and periodic features of physiological rhythms, and therefore lead to reduced estimation accuracy. To overcome these issues, we propose StreamPPG, a unified architecture that enables low-latency frame-wise physiological signal estimation while achieving competitive accuracy compared with clip-wise approaches. StreamPPG is trained under a consistent privileged learning (CPL) strategy, which leverages ground-truth rPPG signals as privileged information to enhance the model's representation capability. Extensive experiments demonstrate that StreamPPG achieves state-of-the-art accuracy across multiple datasets while maintaining real-time throughput on edge devices.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。