Towards passive heart health monitoring via smartphone camera

Heart rate (HR), one of the cardinal vital signs, is a dynamic indicator of physiological status, influenced by everything from activity, to stress, to acute and chronic illness. Further, resting heart rate (RHR) is a key biomarker of cardiovascular health and long-term health risk. A higher RHR and increases in RHR over time are associated with major adverse cardiovascular events and all-cause mortality.Wearables, such as Fitbit devices and the Pixel Watch, have made it possible to track these health markers throughout our daily lives. However, there is room to improve their adoption, especially in low-resource environments and among those most at risk for cardiovascular disease. Smartphones present a unique opportunity to broaden access to health tracking — today, around five billion people already own a device with powerful sensors capable of monitoring their health. In 2022, we demonstrated using smartphones for on-demand HR measurement via a finger placed over the camera, and subsequent Google research considered how the signal detected during that measurement could help predict cardiovascular disease.In “Passive Heart Rate Monitoring During Smartphone Use in Everyday Life”, published in Nature, we introduce a research system (PHRM) that enables tracking of HR and RHR in the background during everyday smartphone use. PHRM leverages the front-facing camera to capture video of the user’s face in the seconds after face unlock events. It then applies deep learning to estimate HR with a mean absolute percentage error (MAPE) < 10% compared to electrocardiogram-derived ground truth, meeting industry accuracy standards for people of all skin tones. Finally, the system integrates HR measurements throughout the day into an estimate of daily RHR that matches the accuracy of wearables, with a mean absolute error (MAE) of < 5 beats per minute (bpm) compared to a wearable tracker. With our publication, we release the largest and most diverse dataset of smartphone videos publicly available for research along with a pre-trained “PHRM-mini” model. Qualified researchers can apply for access.

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