用智能手表传感器被动识别高血压信号

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: Photoplethysmography (PPG): An optical sensor that tracks changes in blood volume at the wrist by measuring reflected light that captures pulse wave contours. Accelerometry (ACC): A motion sensor that identifies periods of stillness and captures the ballistocardiogram (BCG) , the subtle physical vibrations of the wrist caused by the mechanical ejection of blood from the heart into the aorta. The AI system uses photoplethysmography (PPG) and accelerometer (ACC) signals recorded using a consumer smartwatch to create waveform embeddings. The waveform embeddings are aggregated over time (days) into a person-level embedding, which, along with a user’s height, are used to predict their blood pressure status. 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.