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Article

The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring

by 1,2,* and 1
1
Oura Health, Elektroniikkatie 10, 90590 Oulu, Finland
2
Department of Human Movement Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Eng Hock Francis Tay
Sensors 2021, 21(13), 4302; https://doi.org/10.3390/s21134302
Received: 22 May 2021 / Revised: 20 June 2021 / Accepted: 22 June 2021 / Published: 23 June 2021
Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, and circadian features for sleep stage detection on a large dataset. Four hundred and forty nights from 106 individuals, for a total of 3444 h of combined polysomnography (PSG) and physiological data from a wearable ring, were acquired. Features were extracted to investigate the relative impact of different data streams on 2-stage (sleep and wake) and 4-stage classification accuracy (light NREM sleep, deep NREM sleep, REM sleep, and wake). Machine learning models were evaluated using a 5-fold cross-validation and a standardized framework for sleep stage classification assessment. Accuracy for 2-stage detection (sleep, wake) was 94% for a simple accelerometer-based model and 96% for a full model that included ANS-derived and circadian features. Accuracy for 4-stage detection was 57% for the accelerometer-based model and 79% when including ANS-derived and circadian features. Combining the compact form factor of a finger ring, multidimensional biometric sensory streams, and machine learning, high accuracy wake-sleep detection and sleep staging can be accomplished. View Full-Text
Keywords: sleep staging; wearables; heart rate variability; accelerometer; machine learning sleep staging; wearables; heart rate variability; accelerometer; machine learning
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MDPI and ACS Style

Altini, M.; Kinnunen, H. The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring. Sensors 2021, 21, 4302. https://doi.org/10.3390/s21134302

AMA Style

Altini M, Kinnunen H. The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring. Sensors. 2021; 21(13):4302. https://doi.org/10.3390/s21134302

Chicago/Turabian Style

Altini, Marco, and Hannu Kinnunen. 2021. "The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring" Sensors 21, no. 13: 4302. https://doi.org/10.3390/s21134302

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