Deep Learning for Sleep Heart Rate Estimation from Accelerometers: Toward Population-Scale Cardiac Insight Without Optical Sensors
Estimating heart rate from wrist motion during sleep without optical sensors
Researchers developed SeqSmoother, an AI system that estimates heart rate during sleep using only the motion data that wrist accelerometers already collect, rather than requiring optical heart-rate sensors. The system achieved an average error of 1.60 beats per minute and successfully produced estimates for a larger share of sleep recordings than existing methods, though with slightly lower accuracy on the estimates it did produce.
Millions of people wear wrist devices that track motion but lack optical heart sensors. This approach recovers cardiac information from data already being collected, making large population studies of sleep and heart health feasible without expensive hardware upgrades. For researchers analyzing existing accelerometer datasets from large studies, this means cardiac insights are now recoverable from data already in hand.