内容来源：https://research.google/blog/detecting-signs-of-hypertension-using-wearable-sensors/

内容总结：

谷歌研究团队近日发布了一项新的人工智能系统，可利用普通消费级智能手表传感器被动检测高血压迹象，无需传统袖带校准。相关研究成果已发表于《自然》杂志。

高血压常被称为“沉默杀手”，全球患者超过十亿，但逾半数成年人并不知晓自身患病。由于高血压极少表现出明显症状，通常只能通过偶然筛查发现。这一问题在18至44岁年轻人群中尤为突出，漏诊可能延误挽救生命的早期干预时机，并显著增加中风、心力衰竭等不良心血管事件的长期风险。

目前，诊室血压测量是发现高血压的主要初筛途径。近年来，无袖带可穿戴设备被视为补充间歇性诊室诊疗的潜在工具，但现有商业方案通常需要定期使用传统臂式袖带进行校准，仅能追踪相对于袖带基线读数的变化，无法识别绝对高血压状态，限制了其作为独立筛查工具的应用价值。

在这项新研究中，谷歌研究团队提出了名为AI-PPG-ACC-HTN的多模态AI系统，能够利用消费级智能手表被动检测高血压迹象，且无需任何袖带校准。该系统以24小时动态血压监测这一临床金标准为参照进行验证，在初筛和确诊筛查中均达到了与传统袖带诊断流程相当的准确度水平。相关研究发现为谷歌健康应用中“健康守护”功能的血压趋势提供了参考。

该系统的设计思路并非估算某一时刻的精确收缩压和舒张压数值——这类数值极易受瞬时环境噪声干扰——而是聚焦于通过聚合多日的被动生理特征，识别个体潜在的持续性高血压状态。

系统利用现代智能手表上已配备的两种传感器数据，采用两阶段机器学习方法构建。首先，研究团队使用自监督对比学习训练了一维深度卷积神经网络，分析了来自2109名知情同意参与者的492347小时传感器数据，使模型学会将同一人身上重叠的15秒光电容积脉搏波和加速度波形在潜在空间中映射得彼此接近，从而在无需人工特征工程或医学标注的情况下发现血管和心脏机械功能的复杂生理特征。随后，每位用户的15秒特征块在多日佩戴期间进行均值聚合，以消除突发运动或温度变化等瞬时噪声，再将聚合后的个体特征与其身高（作为动脉路径长度的可靠代理指标）结合，输入正则化逻辑回归分类器，计算连续的高血压概率评分。

为评估该系统，研究团队在全美五个中心开展了一项前瞻性多中心临床研究，共纳入196名无已知心血管疾病且未服用降压药物的多元化参与者。研究以24小时动态血压监测为金标准，在初筛和确诊测试两种临床场景下评估智能手表系统的表现。

在初筛场景中，智能手表达到65.8%的中等灵敏度和90.0%的高特异度，阳性预测值高达80.6%。相比之下，以金标准为参照，诊室初筛的灵敏度为55.3%，阳性预测值为77.8%。

对于在诊室初筛呈阳性的个体，现行指南建议进行诊室外确认性监测。研究团队分析了初始诊室血压升高的参与者子集（48人），考察智能手表确认持续性高血压的能力。该多模态系统达到78.4%的中等灵敏度、90.9%的高特异度和96.7%的高阳性预测值。相比之下，复诊诊室血压测量的灵敏度为67.6%、特异度为63.6%、阳性预测值为86.2%。此外，智能手表方案与多日家庭监测相比也具有竞争力，后者灵敏度为89.2%、特异度为81.8%、阳性预测值为94.3%。

综合来看，结果表明该系统在现有临床框架内检测高血压的准确度与传统袖带血压设备相当。

为理解AI模型可能关注的信号特征，研究团队可视化了高血压预测概率最高和最低用户的平均脉搏波形，观察到以下视觉差异：不同评分组的平均脉搏波形、对应的加速度波形（显示心冲击图）、脉搏波形一阶和二阶导数之间存在特征性差异，包括脉搏起始点、峰值、重搏切迹、舒张峰等关键标志点，以及心冲击图中的H、I、J、K、L、M、N波和脉搏二阶导数中的a、b、c、d、e波。

基于智能手表的高血压筛查并非旨在替代标准临床臂式袖带或提供独立的确定性医学诊断，而是指向一种可高度扩展的被动式机会性筛查工具的潜力。该工作属于早期可行性研究，所述方法纯属探索性质，不代表任何现有或即将推出的商业产品功能或设备能力。通过温和提醒高风险个体前往诊所进行正式评估，这项技术有望显著缩小全球高血压知晓差距，在高血压造成不可逆心血管损伤之前及早发现。

该研究由谷歌研究院、贝斯以色列女执事医疗中心和哈佛医学院合作完成。

中文翻译：

2026年11月5日
Paolo Di Achille，员工研究科学家；Ming-Zher Poh，高级员工研究科学家，Google Research

我们提出了一种新的AI系统，利用标准消费级智能手表传感器被动检测高血压迹象。

高血压常被称为“沉默的杀手”，全球有超过十亿人受其影响，但超过半数成年患者并不自知。由于高血压很少表现出明显症状，通常只能通过偶然筛查发现。这一认知缺口在18至44岁的年轻成年人中尤为严重，漏诊可能延误挽救生命的早期干预，并显著增加卒中或心力衰竭等不良心血管事件的长期风险。

诊室血压测量是发现高血压的主要初始筛查入口。近年来，无袖带可穿戴设备已成为补充偶发性诊室诊疗的潜在工具。然而，现有商业解决方案通常需要使用传统上臂袖带进行定期校准。这意味着它们只能追踪相对于袖带基线读数的变化，而无法识别绝对高血压状态，限制了其作为独立筛查工具的效用。

在发表于《Nature》的一项新研究中，我们提出了一种多模态人工智能（AI）系统，能够利用消费级智能手表被动检测高血压迹象，且无需任何袖带校准。该系统名为AI-PPG-ACC-HTN，我们以24小时动态血压监测（ABPM）这一临床金标准对其进行验证，其在初始筛查和确认性筛查中的准确度均与传统基于袖带的诊断流程相当。这些研究发现为Google Health应用中的血压趋势Health Guardian功能提供了参考。

我们的系统并不试图估算精确的、特定时间点的收缩压和舒张压值——这些数值极易受到瞬时环境噪声的影响——而是重新定义了问题。它专注于通过聚合多天的被动生理特征来识别个体潜在的、持续的高血压状态。

该系统利用现代智能手表上已激活的两个传感器的数据：

为构建该系统，我们采用了双阶段机器学习方法：

我们使用自监督对比学习训练了一个一维深度卷积神经网络。通过分析来自2，109名知情同意参与者的492，347小时传感器数据的大型无标签数据集，模型学会了在潜在空间中将来自同一个体的重叠15秒PPG和ACC波形映射得彼此接近。这使模型能够在无需人工特征工程或医学标签的情况下，发现血管和机械心脏功能的复杂、生理信息丰富的特征。

对于每个用户，这些15秒特征块（嵌入向量）在多天佩戴期间进行均值聚合，以消除突然运动或温度变化等短暂噪声。最后，个体的聚合嵌入与其身高（作为动脉路径长度的可靠代理指标）相结合，输入正则化逻辑回归分类器，计算出连续的高血压概率评分。

为评估我们的系统，我们在美国五个站点开展了一项前瞻性、多中心临床研究，涉及196名知情同意的多元化参与者，这些人无已知心血管疾病且未服用降压药物。

重要的是，我们以24小时动态血压监测（ABPM）——高血压诊断的临床金标准——作为智能手表性能的基准。我们在两个临床使用场景中评估了智能手表系统：初始筛查和确认性检测。

智能手表达到了65.8%的中等灵敏度和90.0%的高特异度，产生了80.6%的高阳性预测值（PPV）。相比之下，以金标准为基准，初始诊室筛查的灵敏度为55.3%，PPV为77.8%（中等水平）。

对于在诊所初始筛查为高血压阳性的个体，当前指南建议进行诊室外确认性监测。我们观察了初始诊室血压升高的参与者子集（N=48），以了解智能手表确认持续性高血压的效果如何。

我们的多模态系统达到了78.4%的中等灵敏度、90.9%的高特异度和96.7%的高PPV。相比之下，复诊诊室血压测量的灵敏度（67.6%）、特异度（63.6%）和PPV（86.2%）点估计值均较低。此外，智能手表方法与多日家庭监测相比具有竞争力，后者灵敏度为89.2%，特异度为81.8%，PPV为94.3%。

综合来看，结果表明我们的系统能够在现有临床框架内以与传统袖带血压设备相当的准确度检测高血压。

为了解AI模型可能关注的内容，我们可视化了高血压预测概率最高和最低用户的平均脉搏波形。

我们观察到以下视觉差异：

来自AI-PPG-ACC-HTN评分最高和最低五分位组的四组平均脉搏波形（左上图）、对应的加速度计波形显示心冲击图（BCG，左下图）、脉搏波形的一阶导数（中图）和二阶导数（右图）。图中标注了关键基准点，包括PPG波形中的脉搏起始点（ons）、峰值（sys）、重搏切迹（dic）、舒张峰（dia），BCG波形中的H、I、J、K、L、M、N波，以及PPG波形二阶导数中的a、b、c、d、e波。

基于智能手表的高血压筛查并非旨在取代标准临床上臂袖带或提供确定性独立医学诊断。相反，这项研究指向了一种高度可扩展的被动工具用于机会性筛查的潜力。这项工作代表早期可行性研究，此处描述的方法纯属探索性质，不代表任何现有或即将推出的商业产品功能或设备能力。通过温和地提示高风险个体前往诊所进行正式评估，这项技术有望显著缩小全球认知差距，在高血压造成永久性心血管损害之前加以发现。

这项研究是Google Research、Beth Israel Deaconess Medical Center和Harvard Medical School的合作成果。我们感谢论文共同作者Lawrence Cai、Jiang Wu、Mingwu Gao、Bhavna Daryani、Jonathan Wang、Utkarsh Khanna、Ho Ko、Anupam Pathak、Mark Malhotra、Shwetak Patel、Jacqueline B. Shreibati、Stephen P. Juraschek、Pramod Rudrapatna和Matthew Thompson。我们感谢重要贡献者，包括：Jennifer Block、Anu Gabbita、Sushalini Sunku、Keerthana Natarajan、Alexandros Pantelopoulos、Tuan Phan、Tracy Giest和Florence Thng。

英文来源：

November 5, 2026
Paolo Di Achille, Staff Research Scientist, and Ming-Zher Poh, Senior Staff Research Scientist, Google Research
We present a new AI system that leverages standard consumer smartwatch sensors to passively detect signs of high blood pressure.
Hypertension (i.e., high blood pressure), often called the "silent killer”, affects over one billion people globally, yet more than half of adults with the condition are unaware they have it. Because it rarely presents with obvious symptoms, high blood pressure (BP) is typically discovered only through incidental screening. This gap in awareness is particularly severe among younger adults aged 18 to 44, where missing diagnoses can delay life-saving early interventions and significantly increase the long-term risk of adverse cardiovascular events like stroke or heart failure.
Office BP measurement serves as the primary initial screening gateway for detecting hypertension. In recent years, cuffless wearable devices have emerged as a potential tool to complement episodic office care. However, existing commercial solutions usually require regular calibration using a traditional arm cuff. This means they only track relative changes from a baseline cuff reading rather than identifying absolute hypertensive states, limiting their utility as independent screening tools.
In a new study published in Nature, we present a multimodal artificial intelligence (AI) system capable of passively detecting signs of hypertension using a consumer smartwatch, without requiring any cuff calibration. Dubbed AI-PPG-ACC-HTN, we validated this system against the clinical gold standard of 24-hour ambulatory blood pressure monitoring (ABPM), and it achieves accuracy levels comparable to traditional cuff-based diagnostic workflows for both initial and confirmatory screening. These research findings helped inform the blood pressure trends Health Guardian feature in the Google Health app.
Instead of attempting to estimate exact, point-in-time systolic and diastolic blood pressure values, which are highly susceptible to transient environmental noise, our system reframes the problem. It focuses on identifying an individual’s underlying, persistent hypertensive state by aggregating passive physiological signatures over multiple days.
The system leverages data from two sensors already active on modern smartwatches:
To build this system, we used a two-stage machine learning approach:
We trained a 1-D deep convolutional neural network using self-supervised contrastive learning. By analyzing a large, unlabeled dataset of 492,347 hours of sensor data from 2,109 consenting participants, the model learned to map overlapping 15-second PPG and ACC waveforms from the same individual close together in a latent space. This allowed the model to discover complex, physiologically rich features of vascular and mechanical cardiac function without requiring manual feature engineering or medical labels.
For each user, these 15-second feature blocks (embeddings) are mean-aggregated across multiple days of wear to wash out ephemeral noise like sudden movements or temperature changes. Finally, the individual's aggregated embedding is combined with their height (which serves as a reliable proxy for arterial path length) and fed into a regularized logistic regression classifier to calculate a continuous hypertension probability score.
To evaluate our system, we conducted a prospective, multi-center clinical study across five US sites involving 196 consenting diverse participants who were free from known cardiovascular disease and not taking blood pressure medications.
Importantly, we benchmarked the smartwatch's performance against 24-hour ambulatory blood pressure monitoring (ABPM), the clinical gold standard for hypertension diagnosis. We evaluated the smartwatch system across two clinical use case scenarios: initial screening and confirmatory testing.
The smartwatch achieved a moderate sensitivity of 65.8% and a high specificity of 90.0%, resulting in a high positive predictive value (PPV) of 80.6%. In comparison, initial office-based screenings showed a sensitivity of 55.3% and a moderate PPV of 77.8% when benchmarked against the gold standard.
For individuals who initially screen positive for high blood pressure in a clinic, current guidelines recommend confirmatory out-of-office tracking. We looked at the subset of participants (N=48) who had elevated initial office BP to see how well the smartwatch could confirm sustained hypertension.
Our multimodal system achieved a moderate sensitivity of 78.4%, a high specificity of 90.9% and a high PPV of 96.7%. In comparison, the performance of return office BP measurements showed lower point estimates of sensitivity (67.6%), specificity (63.6%), and PPV (86.2%). Furthermore, the smartwatch approach proved competitive with multi-day home monitoring, which yielded a sensitivity of 89.2%, specificity of 81.8%, and a PPV of 94.3%.
Taken together, the results show that our system can detect hypertension with accuracy levels comparable to traditional cuffed BP devices in the existing clinical framework.
To gain an understanding of what the AI model may be paying attention to, we visualized the average pulse waveforms from users with the highest and lowest predicted probabilities of hypertension.
We observed the following visual differences:
Mean pulse waveforms (top left panel), corresponding accelerometer waveforms showing the ballistocardiogram (BCG, bottom left panel), first (middle panel) and second derivatives (right panel) of pulse waveforms from four of the most extreme quintiles of AI-PPG-ACC-HTN scores. Key fiducial points are indicated including the pulse onset (ons), peak (sys), dicrotic notch (dic), diastolic peak (dia) in the PPG waveform, H, I, J, K, L, M, N waves in the BCG waveform, and a, b, c, d, e waves in the second derivative of the PPG waveform.
Smartwatch-based hypertension screening is not intended to replace standard clinical arm cuffs or provide a definitive standalone medical diagnosis. Instead, this research points to the potential for a highly scalable, passive tool for opportunistic screening. This work represents early feasibility research and methodologies described here are purely exploratory and are not indicative of any existing or upcoming commercial product feature or device capability. By gently prompting at-risk individuals to step into a clinic for formal assessment, this technology holds the potential to significantly close the global awareness gap and catch hypertension before it causes permanent cardiovascular damage.
This research was a collaborative effort between Google Research, Beth Israel Deaconess Medical Center, and Harvard Medical School. We thank our paper co-authors Lawrence Cai, Jiang Wu, Mingwu Gao, Bhavna Daryani, Jonathan Wang, Utkarsh Khanna, Ho Ko, Anupam Pathak, Mark Malhotra, Shwetak Patel, Jacqueline B. Shreibati, Stephen P. Juraschek, Pramod Rudrapatna, and Matthew Thompson. We thank key contributors including: Jennifer Block, Anu Gabbita, Sushalini Sunku, Keerthana Natarajan, Alexandros Pantelopoulos, Tuan Phan, Tracy Giest, and Florence Thng.