ArticleScientific reports2017
Automated tracking to measure behavioural changes in pigs for health and welfare monitoring.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it, 169 citations in OpenAlex.
- A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare.Frontiers in veterinary science · 2021Pooled it
- Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review.PloS one · 2019Pooled it
- Advances in Audio Classification and Artificial Intelligence for Respiratory Health and Welfare Monitoring in Swine.Biology · 2026Review
- StaticPigDetv2: Performance Improvement of Unseen Pig Monitoring Environment Using Depth-Based Background and Facility Information.Sensors (Basel, Switzerland) · 2026Article
- Developing and Assessing the Performance of a Machine Learning Model for Analyzing Drinking Behaviors in Minipigs for Experimental Research.Sensors (Basel, Switzerland) · 2026Article
- EnhancedMulti-Scenario Pig Behavior Recognition Based on YOLOv8n.Animals : an open access journal from MDPI · 2025Article
- Insights into thermal stress effects on performance and behavior of grazing cattle via multimodal sensor monitoring.Scientific reports · 2025Article
- Revealing the Hidden Social Structure of Pigs with AI-Assisted Automated Monitoring Data and Social Network Analysis.Animals : an open access journal from MDPI · 2025Article
- ASAS-NANP Symposium: mathematical modeling in animal nutrition: harnessing real-time data and digital twins for precision livestock farming.Journal of animal science · 2025Review
- How do pig veterinarians view technology-assisted data utilisation for pig health and welfare management? A qualitative study in Spain, the Netherlands, and Ireland.Porcine health management · 2024Article
- Tracking and Behavior Analysis of Group-Housed Pigs Based on a Multi-Object Tracking Approach.Animals : an open access journal from MDPI · 2024Article
- Early detection of bovine respiratory disease in pre-weaned dairy calves using sensor based feeding, movement, and social behavioural data.Scientific reports · 2024Article
- Sow serenity: automatic long-term measurement of lying behavior in crates and free-farrowing pens using 3D accelerometers.Journal of animal science · 2024Article
- Data recording and use of data tools for pig health management: perspectives of stakeholders in pig farming.Frontiers in veterinary science · 2024Article
- Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition.PloS one · 2024Article
- Estimation of gestating sows' welfare status based on machine learning methods and behavioral data.Scientific reports · 2023Article
- Farmers' Perspectives of the Benefits and Risks in Precision Livestock Farming in the EU Pig and Poultry Sectors.Animals : an open access journal from MDPI · 2023Review
- Automatic Detection of Group Recumbency in Pigs via AI-Supported Camera Systems.Animals : an open access journal from MDPI · 2023Article
- Passive radio frequency identification and video tracking for the determination of location and movement of broilers.Poultry science · 2023Article
- A Novel Improved YOLOv3-SC Model for Individual Pig Detection.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Since animals express their internal state through behaviour, changes in said behaviour may be used to detect early signs of problems, such as in animal health. Continuous observation of livestock by farm staff is impractical in a commercial setting to the degree required to detect behavioural changes relevant for early intervention. An automated monitoring system is developed; it automatically tracks pig movement with depth video cameras, and automatically measures standing, feeding, drinking, and locomotor activities from 3D trajectories. Predictions of standing, feeding, and drinking were validated, but not locomotor activities. An artificial, disruptive challenge; i.e., introduction of a novel object, is used to cause reproducible behavioural changes to enable development of a system to detect the changes automatically. Validation of the automated monitoring system with the controlled challenge study provides a reproducible framework for further development of robust early warning systems for pigs. The automated system is practical in commercial settings because it provides continuous monitoring of multiple behaviours, with metrics of behaviours that may be considered more intuitive and have diagnostic validity. The method has the potential to transform how livestock are monitored, directly impact their health and welfare, and address issues in livestock farming, such as antimicrobial use.
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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.