Nightbeat: Heart Rate Estimation From a Wrist-Worn Accelerometer During Sleep

TL;DR

This study demonstrates that wrist-worn accelerometers can accurately estimate heart rate during sleep using a novel frequency-based method, enabling health insights from existing activity data without optical sensors.

Contribution

The paper introduces the first dataset of wrist accelerometer and ECG recordings in at-home sleep settings and proposes a new frequency-based approach for IMU-only heart rate estimation.

Findings

Achieved a mean absolute error of 0.88 bpm in HR estimation

Outperformed previous methods by 76% in accuracy

Validated IMU-only HR estimation as a health indicator

Abstract

Today's fitness bands and smartwatches typically track heart rates (HR) using optical sensors. Large behavioral studies such as the UK Biobank use activity trackers without such optical sensors and thus lack HR data, which could reveal valuable health trends for the wider population. In this paper, we present the first dataset of wrist-worn accelerometer recordings and electrocardiogram references in uncontrolled at-home settings to investigate the recent promise of IMU-only HR estimation via ballistocardiograms. Our recordings are from 42 patients during the night, totaling 310 hours. We also introduce a frequency-based method to extract HR via curve tracing from IMU recordings while rejecting motion artifacts. Using our dataset, we analyze existing baselines and show that our method achieves a mean absolute error of 0.88 bpm -- 76% better than previous approaches. Our results validate the potential of IMU-only HR estimation as a key indicator of cardiac activity in existing longitudinal studies to discover novel health insights. Our dataset, Nightbeat-DB, and our source code are available on GitHub: https://github.com/eth-siplab/Nightbeat.