这项研究解决了PPG血压估计中忽视个体血管差异和形态特征的问题，做可穿戴健康监测的团队可以直接参考其轻量模型设计，校准场景下误差大幅降低，值得关注。
该研究提出了一种名为DMT的Transformer网络，用于从PPG信号进行无袖带血压估计。模型通过FiLM风格的特征调制，将人口统计信息（如年龄、性别）嵌入Transformer的注意力与前馈子层，实现个性化表征学习。同时，引入辅助形态学头，引导模型关注与动脉硬化和波反射相关的波形形态，避免仅依赖振幅主导的捷径。在PulseDB数据集上，校准评估协议下，收缩压MAE为4.56 mmHg，舒张压为2.62 mmHg，相比此前人口统计增强的PPG基线，误差分别降低47%和50%。该轻量单传感器模型支持可扩展的临床级无袖带血压监测。
DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals
Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cost, wearable-friendly cuffless BP estimation. However, even with recent progress, many PPG-based models are trained with BP regression alone and may rely on amplitude-dominated shortcuts. In addition, demographic covariates that systematically modulate vascular compliance are often incorporated only via late fusion, limiting subject-specific representation learning. We propose a Transformer-based network for cuffless BP estimation from PPG signal, leveraging self-attention to capture long-range dependencies across multiple cardiac cycles. To account for subject-specific vascular differences, the model is conditioned on demographics via FiLM-style feature modulation applied through the attention and feed-forward sublayers of Transformer blocks. In addition, we add an auxiliary morphology head to guide the model to attend to BP-relevant waveform morphology associated with arterial stiffness and wave reflection. Under calibration-based evaluation protocols on the large-scale PulseDB dataset, the proposed method achieves MAE of 4.56 mmHg for systolic BP and 2.62 mmHg for diastolic BP, reducing errors by 47% and 50% compared with prior demographic-enhanced PPG baselines. The resulting lightweight, single-sensor model supports scalable and clinically grounded cuffless BP estimation in calibration-enabled deployment settings.