提出轻量级rPPG算法，实现低延迟高精度非接触心率监测。
Memory-efficient Low-latency Remote Photoplethysmography through Temporal-Spatial State Space Duality
- 利用时空状态空间对偶性，高效捕捉面部微变化。
- 跨数据集误差最低至0.25，推理仅需3.6MB内存和9.46ms延迟。
- 适合移动端实时生理监测，已开源代码与演示。
远程光电容积脉搏波描记术（rPPG）通过分析面部光反射实现非接触式生理监测，但深度学习虽提升性能，却带来高昂资源消耗。本文提出ME-rPPG，基于时空状态空间对偶性，解决模型可扩展性、跨数据集泛化性和实时性之间的三难困境。通过可迁移的状态空间，该方法高效捕捉面部帧间的微弱周期性变化，支持长视频训练并保持极低计算开销，实现低延迟推理。在跨数据集测试中，平均绝对误差分别为5.38（MMPD）、0.70（VitalVideo）和0.25（PURE），优于所有基线方法，提升幅度达21.3%至60.2%。系统实现仅3.6MB内存占用和9.46ms延迟，真实部署中相较现有方法提升19.5%-49.7%准确率及43.2%用户满意度。代码与演示已公开于https://health-hci-group.github.io/ME-rPPG-demo/。
原文摘要 · Abstract (English)
Remote photoplethysmography (rPPG), enabling non-contact physiological monitoring through facial light reflection analysis, faces critical computational bottlenecks as deep learning introduces performance gains at the cost of prohibitive resource demands. This paper proposes ME-rPPG, a memory-efficient algorithm built on temporal-spatial state space duality, which resolves the trilemma of model scalability, cross-dataset generalization, and real-time constraints. Leveraging a transferable state space, ME-rPPG efficiently captures subtle periodic variations across facial frames while maintaining minimal computational overhead, enabling training on extended video sequences and supporting low-latency inference. Achieving cross-dataset MAEs of 5.38 (MMPD), 0.70 (VitalVideo), and 0.25 (PURE), ME-rPPG outperforms all baselines with improvements ranging from 21.3% to 60.2%. Our solution enables real-time inference with only 3.6 MB memory usage and 9.46 ms latency -- surpassing existing methods by 19.5%-49.7% accuracy and 43.2% user satisfaction gains in real-world deployments. The code and demos are released for reproducibility on https://health-hci-group.github.io/ME-rPPG-demo/.
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