ArticleScientific reports2021
Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures.
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 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Seasonal variations in the polysomnographic sleep/wake cycle and activity/rest pattern in the dromedary camel (Camelus dromedarius).Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology · 2026Article
- STELLAR-CB: Synthetic Temporal LSTM for Livestock Activity Recognition-Cow Behaviour.Veterinary medicine and science · 2026Article
- AI-Driven Multimodal Sensing for Early Detection of Health Disorders in Dairy Cows.Animals : an open access journal from MDPI · 2026Article
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- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Methods used for estimating sleep in dairy cattle.JDS communications · 2024Article
- Sleep stage prediction using multimodal body network and circadian rhythm.PeerJ. Computer science · 2024Article
- Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study.Sensors (Basel, Switzerland) · 2021Review
- Heart Rate and Heart Rate Variability Change with Sleep Stage in Dairy Cows.Animals : an open access journal from MDPI · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep stages are difficult and impractical to use with cattle in typical farm environments. The objective of this study was to determine if data from more easily applied non-invasive devices assessing neck muscle activity and heart rate (HR) alone could be used to differentiate between sleep stages. We developed, trained, and compared two machine learning models using neural networks and random forest algorithms to predict sleep stages from 15 variables (features) of the muscle activity and HR data collected from 12 cows in two environments. Using k-fold cross validation we compared the success of the models to the gold standard, Polysomnography (PSG). Overall, both models learned from the data and were able to accurately predict sleep stages from HR and muscle activity alone with classification accuracy in the range of similar human models. Further research is required to validate the models with a larger sample size, but the proposed methodology appears to give an accurate representation of sleep stages in cattle and could consequentially enable future sleep research into conditions affecting cow sleep and welfare.
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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.