用轻量算法实现高精度远程心率监测，实时运行无负担。
FacePhys: State of the Heart Learning
- 基于时空状态对偶设计，兼顾精度与效率
- 误差降低49%，推理延迟仅9.46毫秒
- 适合移动端部署，支持长时间视频分析
利用摄像头进行生命体征测量为无感、普适的健康监测提供了可能。远程光电容积脉搏波图（rPPG）作为核心技术，可通过皮肤反射光的微小变化实现心脏活动检测。然而，实际应用受限于前端设备的计算资源以及压缩传输通道导致的信号质量下降。本文提出一种内存高效的rPPG算法——FacePhys，基于时-空状态空间对偶性，解决了模型可扩展性、跨数据集泛化能力和实时运行之间的三难困境。通过可迁移的心脏状态建模，FacePhys在保持极低计算开销的同时，捕捉视频帧间细微的周期性变化，支持长序列训练和低延迟推理。实验表明，该方法误差降低49%，实现3.6 MB内存占用和每帧9.46毫秒的推理延迟，较现有方法提升83%至99%。结果验证了其在实际部署中的可靠性，现场演示见https://www.facephys.com/。
原文摘要 · Abstract (English)
Vital sign measurement using cameras presents opportunities for comfortable, ubiquitous health monitoring. Remote photoplethysmography (rPPG), a foundational technology, enables cardiac measurement through minute changes in light reflected from the skin. However, practical deployment is limited by the computational constraints of performing analysis on front-end devices and the accuracy degradation of transmitting data through compressive channels that reduce signal quality. We propose a memory efficient rPPG algorithm - \emph{FacePhys} - built on temporal-spatial state space duality, which resolves the trilemma of model scalability, cross-dataset generalization, and real-time operation. Leveraging a transferable heart state, FacePhys captures subtle periodic variations across video frames while maintaining a minimal computational overhead, enabling training on extended video sequences and supporting low-latency inference. FacePhys establishes a new state-of-the-art, with a substantial 49\% reduction in error. Our solution enables real-time inference with a memory footprint of 3.6 MB and per-frame latency of 9.46 ms -- surpassing existing methods by 83\% to 99\%. These results translate into reliable real-time performance in practical deployments, and a live demo is available at https://www.facephys.com/.
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