想搞可穿戴血压监测？这篇论文用耳夹PPG加ECG，单搏就能估血压，误差才4/1.8 mmHg，比传统方法准28%，代码还开源。
该研究提出一种轻量级多模态可穿戴框架，实现单搏水平的无袖带连续血压估计。系统集成同步胸部心电图和耳夹反射光电容积描记术，每个都配备6轴惯性测量单元提供运动上下文。混合学习架构使用一维卷积神经网络从单个PPG搏动中提取64维嵌入，并与30个生理学特征融合，经LightGBM回归。在10名受试者的多阶段压力协议和PulseDB公共数据集上，模型对收缩压的平均绝对误差为4.02±0.21 mmHg，舒张压为1.79±0.05 mmHg，相比基线模型综合MAE降低28.2%。该框架支持无长时上下文的逐搏估计，适用于资源受限的可穿戴部署。
Single-Beat Cuffless Blood Pressure Estimation Using Ear-PPG and ECG with a Lightweight Hybrid Learning Framework
Continuous cuffless blood pressure (BP) monitoring remains challenging due to motion artifacts, physiological variability, and the limited robustness of conventional pulse transit time (PTT) models under dynamic conditions. Many prior approaches rely on multi-second windows to stabilize estimation, an assumption that is frequently violated during real-world monitoring with intermittent signal corruption. Here, we show that discriminative BP-related information is preserved at the single-beat level and present a lightweight multi-modal wearable framework for continuous BP estimation. The system integrates synchronized chest electrocardiography (ECG) and ear-clip reflectance photoplethysmography, each co-located with a 6-axis inertial measurement unit to provide motion context. We introduce a hybrid learning architecture in which a one-dimensional convolutional neural network extracts a 64-dimensional embedding from individual PPG beats and fuses it with 30 physiology-grounded features, including PTT statistics and heart rate variability, followed by LightGBM regression. The method was evaluated using a multi-phase stress protocol ($n=10$) and the PulseDB public dataset with subject-disjoint validation. Across 30 independent runs, the model achieved mean absolute errors of $4.02 \pm 0.21$~mmHg for systolic BP and $1.79 \pm 0.05$~mmHg for diastolic BP, corresponding to a 28.2\% reduction in combined MAE relative to baseline models. By enabling beat-wise estimation without long temporal context, this framework supports computationally efficient cuffless BP monitoring suitable for wearable deployment under practical resource constraints. The source code for this work is available at https://github.com/SYMBIOX-Lab/BP-wireless.