ArticleScientific reports2021
Efficient embedded sleep wake classification for open-source actigraphy.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Exploring sleep health and circadian rhythm disruption in Sjögren's disease: an accelerometric and self-reported cross-sectional study.Rheumatology (Oxford, England) · 2026Article
- Sleep awake detection from leg-worn wearables using deep sensor fusion.Scientific reports · 2026Article
- Comparison and Validation of Actigraphy Algorithms Using a Large Community Dataset: Algorithm Validation Study.JMIR formative research · 2025Article
- Polysomnography in Transition: Reassessing Its Role in the Future of Sleep Medicine.Journal of sleep research · 2025Review
- Sleep and circadian parameters in Behçet's syndrome: a comparative analysis using actigraphy and questionnaires.Rheumatology (Oxford, England) · 2025Article
- WACSAW: An adaptive, statistical method to classify movement into sleep and wakefulness states.PloS one · 2025Article
- Developing a Sleep Algxorithm to Support a Digital Medicine System: Noninterventional, Observational Sleep Study.JMIR mental health · 2024Observational
- Sleep prediction using data from oximeter, accelerometer and snoring for portable monitor obstructive sleep apnea diagnosis.Scientific reports · 2024Article
- Actigraphic and self-reported characterization of sleep in systemic lupus erythematosus patients.Rheumatology (Oxford, England) · 2024Article
- Exploratory Study on the Associations between Lifetime Post-Traumatic Stress Spectrum, Sleep, and Circadian Rhythm Parameters in Patients with Bipolar Disorder.International journal of environmental research and public health · 2023Article
- Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device.Sensors (Basel, Switzerland) · 2022Article
- Impact of intermittently scanned continuous glucose monitoring with alarms on sleep and metabolic outcomes in children and adolescents with type 1 diabetes.Acta diabetologica · 2022Observational
- Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach.Nature and science of sleep · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents a thorough analysis of sleep/wake detection algorithms for efficient on-device sleep tracking using wearable accelerometric devices. It develops a novel end-to-end algorithm using convolutional neural network applied to raw accelerometric signals recorded by an open-source wrist-worn actigraph. The aim of the study is to develop an automatic classifier that: (1) is highly generalizable to heterogenous subjects, (2) would not require manual features' extraction, (3) is computationally lightweight, embeddable on a sleep tracking device, and (4) is suitable for a wide assortment of actigraphs. Hereby, authors analyze sleep parameters, such as total sleep time, waking after sleep onset and sleep efficiency, by comparing the outcomes of the proposed algorithm to the gold standard polysomnographic concurrent recordings. The relatively substantial agreement (Cohen's kappa coefficient, median, equal to 0.78 ± 0.07) and the low-computational cost (2727 floating-point operations) make this solution suitable for an on-board sleep-detection approach.
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