Apple researchers unlock hidden heart data from Apple Watch sensor using AI

In the era of wearable technology, where a simple wristband can monitor steps, sleep, and even detect irregular heart rhythms, the Apple Watch stands as a beacon of innovation — blending sleek design with life-saving potential. Yet, beneath its optical heart sensor lies untapped power: photoplethysmography (PPG) signals that capture the subtle dance of blood flow through our veins. A groundbreaking new study from Apple researchers illuminates this frontier, revealing how artificial intelligence could unlock richer, more precise cardiac insights from these everyday readings.
Titled “Hybrid Modeling of Photoplethysmography for Non-Invasive Monitoring of Cardiovascular Parameters,” the research demonstrates a hybrid AI approach that sidesteps the limitations of traditional data-driven methods, paving the way for passive, long-term tracking of vital biomarkers like stroke volume and cardiac output. As hypertension notifications in watchOS 26 already hint at this future — potentially alerting over a million undiagnosed cases in its first year — this work doesn’t just advance science; it reimagines the Watch as a silent guardian of heart health, all without invasive tools or costly annotations.
Marcus Mendes for 9to5Mac:
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[T]he researchers demonstrate that it is possible to estimate deeper cardiac metrics using a simple finger pulse sensor, also known as a photoplethysmograph (PPG), the same optical sensing modality used in the Apple Watch (though with different signal characteristics)…
Once the entire training process and model pipeline were in place, they picked an entirely new dataset “comprising APW and PPG signals from 128 patients undergoing non-cardiac surgery, labeled with cardiovascular biomarkers.”
After running this data through the pipeline, they saw that it accurately tracked stroke volume and cardiac output trends, though not their exact absolute values.
Still, their method outperformed conventional techniques, showing that AI-assisted modeling can extract more meaningful heart insights from a simple optical sensor.

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MacDailyNews Take: AI is rapidly changing our world.
Here’s the Apple researchers’ conclusion:
In this work we use a hybrid modeling approach to infer cardiovascular parameters from in-vivo PPG signals. Compared to purely data-driven approaches that struggle due to limited labeled data, our method achieves promising results by incorporating simulations and sidestepping the need for invasive and costly annotations. While other existing hybrid approaches for cardiovascular modeling either embed physical properties as structural constraints within neural networks or augment traditional physiological models with data-driven components, our method incorporates physical knowledge in the model through SBI…
Our results contribute to characterizing the informativeness of PPG signals for predicting cardiac biomarkers, and could extend beyond the ones considered in our experiments. While our results are promising in monitoring temporal trends, absolute value prediction of complex biomarkers remains challenging, and is a key direction for future work. Future work may also explore alternative generative approaches for the PPG-to-APW mapping, or investigate different architectural choices. Finally, a similar learning strategy than the one used here for finger PPG could extend to other modalities, including wearable PPG, and open the door to passive and long-term cardiac biomarker monitoring.

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