What do the Apple Watch and Nokia Pulse Ox have in common? They’ve both got pulse oximeter sensors that measure heart rate using photoplethysmography (PPG), the expansion and contraction of capillaries based on changes in blood volume. They’re accurate to a degree, but require a fair amount of electricity because they’re light-based — they emit a signal onto the skin that reflects back to a photodiode.

One battery-saving alternative might be accelerometers, a sensor commonly found in smartphones, smartwatches, and activity trackers that measures non-gravitational acceleration. In a paper published on the preprint server Arxiv.org, researchers at Philips Health and the University of Bristol describe a machine learning algorithm that can predict heart rate almost exclusively from the sensors, boosting the battery life of the wearable to which they’re attached.

“Consumer PPG sensors typically consume up to 5000 times the power than the accelerometer used in wearables, which is an impediment to the long battery life desired in wearable technology,” the researchers wrote. “As accelerometers are widespread and exist in any device which would likely also contain a heart rate sensor, we are interested in considering the feasibility of acceleration as a means of predicting heart rate.”

They tapped data from test subjects participating in the EurValve project, a multiyear clinical study of patients who have undergone heart valve replacement surgery. Each sports a wearable with an accelerometer (with a three-week battery life) and a Philips Health monitor with a pulse oximeter (with a…

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