Detecting signs of hypertension using wearable sensors

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.

How it works

The system leverages data from two sensors already active on modern smartwatches:

To build this system, we used a two-stage machine learning approach: