Monitoring blood pressure with non-invasive sensors has gained popularity for providing comfortable user experiences, one of which is a significant function of smart wearables. Although providing a comfortable user experience, such methods are suffering from the demand for a significant amount of realistic data to train an individual model for each subject, especially considering the invasive or obtrusive BP ground-truth measurements. To tackle this challenge, we introduce a novel physics-informed temporal network~(PITN) with adversarial contrastive learning to enable precise BP estimation with very limited data. Specifically, we first enhance the physics-informed neural network~(PINN) with the temporal block for investigating BP dynamics' multi-periodicity for personal cardiovascular cycle modeling and temporal variation. We then employ adversarial training to generate extra physiological time series data, improving PITN's robustness in the face of sparse subject-specific training data. Furthermore, we utilize contrastive learning to capture the discriminative variations of cardiovascular physiologic phenomena. This approach aggregates physiological signals with similar blood pressure values in latent space while separating clusters of samples with dissimilar blood pressure values. Experiments on three widely-adopted datasets with different modailties (\emph{i.e.,} bioimpedance, PPG, millimeter-wave) demonstrate the superiority and effectiveness of the proposed methods over previous state-of-the-art approaches. The code is available at~\url{https://github.com/Zest86/ACL-PITN}.

翻译：利用非侵入式传感器监测血压因其提供舒适的用户体验而日益普及，这已成为智能可穿戴设备的重要功能之一。尽管此类方法提升了用户体验，但其需要大量真实数据来为每个受试者训练个体化模型，尤其是在考虑到侵入性或干扰性血压真值测量的情况下。为应对这一挑战，我们提出了一种新颖的物理信息时序网络（PITN），结合对抗对比学习，能够在数据极其有限的情况下实现精确的血压估计。具体而言，我们首先通过时序模块增强物理信息神经网络（PINN），以探究血压动态的多周期性，从而建立个体心血管周期模型并捕捉其时序变化。随后，我们采用对抗训练生成额外的生理时序数据，以提升PITN在面对稀疏个体特异性训练数据时的鲁棒性。此外，我们利用对比学习捕捉心血管生理现象的判别性变化。该方法在潜在空间中将具有相似血压值的生理信号聚合，同时分离具有不同血压值的样本簇。在三种广泛采用的不同模态数据集（即生物阻抗、光电容积脉搏波、毫米波）上的实验表明，所提方法相较于以往最先进方法具有优越性和有效性。代码发布于~\url{https://github.com/Zest86/ACL-PITN}。