ArticleiScience2022
A jerk-based algorithm ACCEL for the accurate classification of sleep-wake states from arm acceleration.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Trial
- Machine-learning classification of bipolar disorder incorporating wearable-derived core body temperature and actigraphy-derived sleep indices.PCN reports : psychiatry and clinical neurosciences · 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
- Study Protocol for the Japan Pregnancy, Eating, Activity, Cohort (J-PEACH) Study: Investigating Perinatal Maternal Lifestyle and Infant Health.Methods and protocols · 2025Article
- Preventive circadian medicine: improving health with sleep checkups.Npj biological timing and sleep · 2025Review
- Sleep Quality in Chronic Pain Patients With and Without Strong Opioid Use: An Observational Study Using a Wearable Device.Neuropsychopharmacology reports · 2025Observational
- Automated sleep staging from single-channel electroencephalogram using hybrid neural network with manual features and attention.iScience · 2025Article
- Use of posterior probabilities from a long short-term memory network for characterizing dance behavior with multiple accelerometers.Journal of Alzheimer's disease : JAD · 2025Article
- Jerks are useful: extracting pulse rate from wrist-placed accelerometry jerk during sleep in children.Sleep · 2025Article
- Detection of sleep apnea using only inertial measurement unit signals from apple watch: a pilot-study with machine learning approach.Sleep & breathing = Schlaf & Atmung · 2025Article
- Development and Calibration of a PATCH Device for Monitoring Children's Heart Rate and Acceleration.Medicine and science in sports and exercise · 2024Article
- Quantitative detection of sleep apnea in adults using inertial measurement unit embedded in wristwatch wearable devices.Scientific reports · 2024Article
- Recent Progress in Long-Term Sleep Monitoring Technology.Biosensors · 2023Review
- An Automated Algorithm for Determining Sleep Using Single-Channel Electroencephalography to Detect Delirium: A Prospective Observational Study in Intensive Care Units.Healthcare (Basel, Switzerland) · 2022Article
- Analysis of wearable time series data in endocrine and metabolic research.Current opinion in endocrine and metabolic research · 2022Review
- The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device's Data.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Arm acceleration data have been used to measure sleep-wake rhythmicity. Although several methods have been developed for the accurate classification of sleep-wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named ACceleration-based Classification and Estimation of Long-term sleep-wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specific functions. The algorithm uses a derivative of triaxial acceleration (jerk), which can reduce individual differences in the variability of acceleration data. Applying a machine learning algorithm to the jerk data achieved sleep-wake classification with a high sensitivity (>90%) and specificity (>80%). A jerk-based analysis also succeeded in recording periodic activities consistent with pulse waves. Therefore, the ACCEL algorithm will be a useful method for large-scale sleep measurement using simple accelerometers in real-world settings.
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