Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and sustained hypertension is an often silent risk factor, making cuffless continuous blood pressure (BP) monitoring with wearable devices important for early screening and long-term management. Most existing cuffless BP estimation methods use only photoplethysmography (PPG) and electrocardiography (ECG) signals, alone or in combination. These models are typically developed under resting or quasi-static conditions and struggle to maintain robust accuracy in multi-motion-state scenarios. In this study, we propose a six-modal BP estimation framework that jointly leverages ECG, multi-channel PPG, attachment pressure, sensor temperature, and triaxial acceleration and angular velocity. Each modality is processed by a lightweight branch encoder, contrastive learning enforces cross-modal semantic alignment, and a mixture-of-experts (MoE) regression head adaptively maps the fused features to BP across motion states. Comprehensive experiments on the public Pulse Transit Time PPG Dataset, which includes running, walking, and sitting data from 22 subjects, show that the proposed method achieves mean absolute errors (MAE) of 3.60 mmHg for systolic BP (SBP) and 3.01 mmHg for diastolic BP (DBP). From a clinical perspective, it attains Grade A for SBP, DBP, and mean arterial pressure (MAP) according to the British Hypertension Society (BHS) protocol and meets the numerical criteria of the Association for the Advancement of Medical Instrumentation (AAMI) standard for mean error (ME) and standard deviation of error (SDE).

翻译：心血管疾病是全球范围内发病率和死亡率的主要原因，而持续性高血压是一种常无症状的风险因素，这使得利用可穿戴设备进行无袖带连续血压监测对于早期筛查和长期管理至关重要。现有的大多数无袖带血压估计方法仅使用光电容积脉搏波信号和心电图信号，单独或组合使用。这些模型通常在静息或准静态条件下开发，难以在多运动状态下保持稳健的准确性。在本研究中，我们提出了一种六模态血压估计框架，联合利用心电图、多通道光电容积脉搏波、附着压力、传感器温度以及三轴加速度和角速度。每种模态通过轻量级分支编码器处理，对比学习强制跨模态语义对齐，而专家混合回归头自适应地将融合特征映射到不同运动状态下的血压。在包含22名受试者跑步、行走和坐姿数据的公开脉搏波传导时间数据集上的综合实验表明，所提方法在收缩压和舒张压上分别达到3.60毫米汞柱和3.01毫米汞柱的平均绝对误差。从临床角度看，根据英国高血压学会协议，该方法在收缩压、舒张压和平均动脉压上均达到A级评级，并满足美国医疗器械促进协会标准关于平均误差和误差标准差的数据标准。