ArticleScientific reports2021
Sleep classification from wrist-worn accelerometer data using random forests.
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 41 papers, 1 of them a synthesis that pooled it.
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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
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of the performance of actigraphy in measuring sleep stages.Journal of sleep research · 2024Pooled it
- Wearable Sleep-Stage Classification: A Systematic Analysis of Signal Modalities, Representations, and Multimodal Fusion.Sensors (Basel, Switzerland) · 2026Article
- Associations Between Accelerometer-Assessed Sleep Patterns, Proteomic Signatures, and Hallmarks of Aging in Adulthood.Aging cell · 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
- Universal approach to actigraphic sleep/wake scoring, verified against 5 classic algorithms on 3 datasets.Scientific reports · 2026Article
- Sleep awake detection from leg-worn wearables using deep sensor fusion.Scientific reports · 2026Article
- What matters beyond model choice for wearable sleep staging? How personalization, evaluation choices, and easy-to-classify wake impact performance.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Skin-Integrated Soft Wearable XR Interfaces for Seamless and Realistic User Experience.Chemical reviews · 2025Review
- SE-Res-U-Net: an improved U-Net architecture for efficient sleep state detection and classification.Scientific reports · 2025Article
- Validation of sleep-wake estimation from thigh-worn accelerometers against polysomnography in adolescents with and without mental disorders.BMC psychiatry · 2025Article
- Predicting Sleep Quality Based on Metabolic, Body Composition, and Physical Fitness Variables in Aged People: Exploratory Analysis with a Conventional Machine Learning Model.Journal of functional morphology and kinesiology · 2025Article
- A novel feature extractor based on constrained cross network for detecting sleep state.Scientific reports · 2025Article
- Insights into endometriosis symptom trajectories and assessment of surgical intervention outcomes using longitudinal actigraphy.NPJ digital medicine · 2025Article
- Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting.Sleep health · 2025Article
- Sex- and social context-dependent differences in mice fine head movement during social interactions.BMC biology · 2025Article
- Automatic time in bed detection from hip-worn accelerometers for large epidemiological studies: The Tromsø Study.PloS one · 2025Article
- Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology.Sensors (Basel, Switzerland) · 2024Article
- Predicting sleep based on physical activity, light exposure, and Heart rate variability data using wearable devices.Annals of medicine · 2024Article
- Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review.Journal of medical Internet research · 2024Article
- AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.Biosensors · 2024Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolution accelerometer data primarily rely on heuristic algorithms. In this paper, we explore the potential of detecting sleep using Random forests. Models were trained using data from three different studies where 134 adult participants (70 with sleep disorder and 64 good healthy sleepers) wore an accelerometer on their wrist during a one-night polysomnography recording in the clinic. The Random forests were able to distinguish sleep-wake states with an F1 score of 73.93% on a previously unseen test set of 24 participants. Detecting when the accelerometer is not worn was also successful using machine learning ([Formula: see text]), and when combined with our sleep detection models on day-time data provide a sleep estimate that is correlated with self-reported habitual nap behaviour ([Formula: see text]). These Random forest models have been made open-source to aid further research. In line with literature, sleep stage classification turned out to be difficult using only accelerometer data.
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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.