Towards Benchmarked Sleep Detection with Wrist-Worn Sensing Units

Borazio, Marko; Berlin, Eugen; Kücükyildiz, Nagihan; Scholl, Philipp M.; Laerhoven, Kristof Van

doi:10.1109/ichi.2014.24

Cited by 50 publications

(48 citation statements)

References 21 publications

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“…As a result, many studies now use wrist-worn accelerometers in an attempt to objectively measure sleep durations [6, 7]. Publicly available sleep detection methods developed for wrist-worn accelerometers include the idleness-detecting method by Borazio et al [8] and the angular-movement based method by van Hees et al [6]. Methods such as these have traditionally been developed in a laboratory environment and tested against the gold standard for objective sleep analysis, polysomnography.…”

Section: Introductionmentioning

confidence: 99%

Automated detection of sleep-boundary times using wrist-worn accelerometry

O’Donnell

Hollowell

Salimi‐Khorshidi

et al. 2017

Preprint

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Objective Current polysomnography-validated measures of sleep status from wrist-worn accelerometers cannot be used in fully automated analysis as they rely on self-reported sleep-onset and -end (sleep-boundary) information. We set out to develop an automated, data-driven approach to sleep-boundary detection from wrist-worn accelerometer data. MethodsOn three separate occasions, participants were asked to wear a GENEActiv ® wrist-worn accelerometer for nine days and concurrently complete sleep diaries with lights-off, asleep and wake-up information. We developed and evaluated three data-driven methods for sleep-boundary detection: a change-point detection based method , a thresholding method and a random forest classifier based method. Mean absolute errors between automatically-derived and self-reported sleep-onset and wake-up times were recorded in addition to kappa statistics for the minute-by-minute performance of each of the methods. ConclusionOur methods provide a data-driven approach to detect sleep-onset and -end times without the need for self-reported sleep-boundary information. The methods are likely

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Section: Introductionmentioning

confidence: 99%

Automated detection of sleep-boundary times using wrist-worn accelerometry

O’Donnell

Hollowell

Salimi‐Khorshidi

et al. 2017

Preprint

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“…Using acceleration data obtained from wristwatches enables high accuracy for the user identification [6] and the wearer's activity recognition [4]. However, dozing detection is more erroneous than detachment detection [7] and sleep detection [8]. As a solution to that, we implemented a function enabling the owner to cancel erroneous detection.…”

Section: Discussionmentioning

confidence: 99%

“…The wearer's sleep and arousal state were detected using 3D inertia data of wrist sensors [8]. Highly accurate detection was possible because the detection was performed at long time intervals.…”

Section: Sleep Detection Using Wrist Sensorsmentioning

confidence: 99%

A Method for Smartphone Theft Prevention When the Owner Dozes Off

Nagata

Seki

2019

IEICE Trans. Inf. & Syst.

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“…Our method is device agnostic, and could be reused in other large sensor datasets 10,11,32,33 , provided model tuning takes place in a relevant population with free-living groundtruth validation tools such as wearable cameras 13,30 . For this study we did not use traditional cumbersome methods to collect sleep 34 and energy expenditure 35 groundtruth data, as we preferred to use proxy reference methods for free-living assessment at scale 35,36 . We have not generalised the overall descriptive findings to the UK population since the UK Biobank was established as an aetiological study rather than one aimed at population surveillance 9,37 .…”

Section: Discussionmentioning

confidence: 99%

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants

Willetts

Hollowell

Aslett

et al. 2017

Preprint

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Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Markov confusion matrices, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 84,616 minutes of recorded free-living behaviours from 57 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,609 participants. For example, we find that men spend more time in both low-and high-intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.

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Towards Benchmarked Sleep Detection with Wrist-Worn Sensing Units

Cited by 50 publications

References 21 publications

Automated detection of sleep-boundary times using wrist-worn accelerometry

Automated detection of sleep-boundary times using wrist-worn accelerometry

A Method for Smartphone Theft Prevention When the Owner Dozes Off

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants

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