AI 中文总结
该研究利用谷歌Pixel Watch采集的数据，提出深度学习模型并融合多模态信号，实现无校准无袖带的血压估计，虽泛化性较好但肥胖个体误差更高，为消费级智能手表用于血压监测提供了可行性。
AI 中文摘要
临床场景外血压（BP）监测与管理不足会加重高血压等主要心血管风险因素。袖带式设备常用于居家监测，但因对体位、上臂压迫敏感且便携性有限，日常使用不便。消费级智能手表是有前景的替代方案，可利用心脏活动相关生理信号在日常生活场景中无创连续估计血压。本研究使用谷歌Pixel Watch从40名参与者收集的数据，开发并比较多种血压估计算法方法，发现所提出的深度学习模型整体性能最强，将智能手表信号与人口统计学信息融合可提升模型对未见个体的泛化性；但也发现模型准确性在不同参与者亚组间不一致，肥胖个体的估计误差高于其他个体。本研究强调消费级智能手表作为部署稳健血压估计算法的可及平台的可行性，不过临床可靠性需要更大规模、更多样化的人群及额外传感模态支持。
英文摘要
Inadequate blood pressure (BP) monitoring and management outside of clinical settings can worsen major cardiovascular risk factors such as hypertension. While cuff-based devices are commonly used for at-home monitoring, these devices can be inconvenient for daily use due to their sensitivity to body positions, upper-arm constrictions, and limited portability. A promising alternative is emerging in the form of consumer-grade smartwatches, where physiological signals related to cardiac activity can be used to estimate BP non-invasively and continuously across daily living conditions. In this work, we use data collected from a Google Pixel Watch in 40 participants to develop and compare several algorithm approaches for BP estimation. We found that our proposed deep learning model achieved the strongest overall performance, and that fusing smartwatch signals with demographic information improved model generalizability to unseen individuals. However, we also identified that model accuracy was not consistent across participant subgroups, with obese individuals yielding higher estimation errors than others. This study highlights the feasibility of consumer-grade smartwatches as accessible platforms for deploying robust BP estimation algorithms, though clinical reliability will require larger, more diverse populations and additional sensing modalities.
