ArticleNPJ digital medicine2019
Automating sleep stage classification using wireless, wearable sensors.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it, 95 citations in OpenAlex.
- A systematic review of the performance of actigraphy in measuring sleep stages.Journal of sleep research · 2024Pooled it
- Wireless wearable sensors can facilitate rapid detection of sleep apnea in hospitalized stroke patients.Sleep · 2024Trial
- Actigraphy Meets AI: A Digital Biomarker for Parkinson's Disease and Isolated REM Sleep Behaviour Disorder.Journal of sleep research · 2026Article
- Wearable Sleep-Stage Classification: A Systematic Analysis of Signal Modalities, Representations, and Multimodal Fusion.Sensors (Basel, Switzerland) · 2026Article
- Accelerometer-derived real-world sleep stages and risk of incident diseases: A UK Biobank cohort study and phenome-wide association analysis.PLoS medicine · 2026Article
- Applying Artificial Intelligence to Expand the Measurement Tool Kit in Clinical-Psychological Science: Moving Beyond Self-Reports.Clinical psychological science : a journal of the Association for Psychological Science · 2026Article
- Polymer-Based Flexible Wireless Sensors for Health Monitoring.Nano-micro letters · 2026Review
- Objective Assessment in Clinical Psychological Science: Progress in Wearable Alcohol Biosensors.Annual review of clinical psychology · 2026Review
- Digital Technology for Sleep Symptoms in Parkinson's Disease: A Scoping Review.Journal of movement disorders · 2026Article
- AI-Driven Sleep Staging Using Instantaneous Heart Rate and Accelerometry: Insights From an Apple Watch Study.IEEE transactions on bio-medical engineering · 2026Article
- Recent Advances in Laser-Induced Graphene-Based Gas Sensors: From Sensing Mechanisms to Biomedical Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Personalized Machine Learning Intervention to Improve Sleep Quality Using Wearable Technology in Healthy Middle-Aged Adults From Mexico City: Protocol for a Pilot Randomized Controlled Trial.JMIR research protocols · 2026Article
- Detecting sleep stages after stroke using wearable sensors: machine learning design and challenges.BMJ digital health & AI · 2026Article
- A skin-interfaced wireless wearable device and data analytics approach for sleep-stage and disorder detection.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Continuous sleep depth index annotation with deep learning yields novel digital biomarkers for sleep health.NPJ digital medicine · 2025Article
- Article
- What radio waves tell us about sleep!Sleep · 2025Article
- NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour.BMC digital health · 2024Article
- Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- An ensemble method for improving robustness against the electrode contact problems in automated sleep stage scoring.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 2 countries.
Funding
Abstract
Polysomnography (PSG) is the current gold standard in high-resolution sleep monitoring; however, this method is obtrusive, expensive, and time-consuming. Conversely, commercially available wrist monitors such as ActiWatch can monitor sleep for multiple days and at low cost, but often overestimate sleep and cannot differentiate between sleep stages, such as rapid eye movement (REM) and non-REM. Wireless wearable sensors are a promising alternative for their portability and access to high-resolution data for customizable analytics. We present a multimodal sensor system measuring hand acceleration, electrocardiography, and distal skin temperature that outperforms the ActiWatch, detecting wake and sleep with a recall of 74.4% and 90.0%, respectively, as well as wake, non-REM, and REM with recall of 73.3%, 59.0%, and 56.0%, respectively. This approach will enable clinicians and researchers to more easily, accurately, and inexpensively assess long-term sleep patterns, diagnose sleep disorders, and monitor risk factors for disease in both laboratory and home settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.