Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Temperature-Dependent Modulation of Cardiac Metabolism, Post-Injury Survival and Regenerative Rate in Axolotls.

Metabolites·2026
Same author

Erratum to "High-throughput screening of ancient forest plant extracts shows cytotoxicity towards triple-negative breast cancer" [Environ. Int. 181 (2023) 108279].

Environment international·2026
Same author

Microbial communication in saline environments: quorum sensing and the future of anaerobic wastewater treatment.

BMC biology·2026
Same author

Time to integrate pets in One Health surveillance.

Lancet (London, England)·2026
Same author

CHD-18 g-modulated Pseudomonas taxa support poplar salt tolerance.

The ISME journal·2026
Same author

Automated Activity Tracking and Space Use Monitoring of Captive Jaguars with Machine Learning.

Animals : an open access journal from MDPI·2026

Related Experiment Video

Updated: Jul 16, 2026

The Use of Traditional Fear Tests to Evaluate Different Emotional Circuits in Cattle
12:08

The Use of Traditional Fear Tests to Evaluate Different Emotional Circuits in Cattle

Published on: April 22, 2020

Behavior Classification of Cattle in a Virtual Fencing System Using Tri-Axial Accelerometers and Machine Learning.

Silje Marquardsen Lund1, Cino Pertoldi1, John Frikke2

  • 1Department of Chemistry and Bioscience, Aalborg University, Frederik Bajers Vej 7H, 9220 Aalborg, Denmark.

Animals : an Open Access Journal From MDPI
|July 15, 2026
PubMed
Summary

Virtual fencing systems use accelerometers to track cattle behavior, finding grazing and reduced rumination near boundaries. Habituated cattle showed no major disruption from virtual fence warnings.

Keywords:
Bos taurusaccelerometerbehavior classificationcattle behaviormachine learningvirtual fencing

More Related Videos

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

Related Experiment Videos

Last Updated: Jul 16, 2026

The Use of Traditional Fear Tests to Evaluate Different Emotional Circuits in Cattle
12:08

The Use of Traditional Fear Tests to Evaluate Different Emotional Circuits in Cattle

Published on: April 22, 2020

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

Area of Science:

  • Animal Behavior
  • Precision Livestock Farming
  • Sensor Technology

Background:

  • Virtual fencing offers a flexible alternative to physical fences in livestock management.
  • Detailed behavioral assessments within virtual fencing systems are limited.
  • Understanding cattle behavior is crucial for optimizing grazing management and animal welfare.

Purpose of the Study:

  • To investigate the use of collar-mounted accelerometers and machine learning to characterize cattle behavior in a virtual fencing system.
  • To estimate individual behavioral time budgets and analyze spatio-temporal activity patterns.
  • To assess the impact of virtual fence warnings on cattle behavior.

Main Methods:

  • Seven Angus cattle were monitored using tri-axial accelerometers, GNSS, and virtual fence warning logs.
  • A random forest classifier was trained to identify behaviors: grazing/feeding, ruminating, lying, standing, and locomotion.
  • Behavioral data were analyzed spatially and in relation to virtual fence interactions.

Main Results:

  • The accelerometer-based model achieved high accuracy (mean 0.87) in classifying key cattle behaviors.
  • Cattle exhibited increased grazing and reduced ruminating near virtual fence boundaries.
  • Habituated cattle showed no consistent short-term behavioral disturbance following virtual fence warnings.

Conclusions:

  • Accelerometer-based behavior classification provides non-invasive, fine-scale insights into cattle behavior in virtual fencing systems.
  • Virtual fencing, in habituated herds, does not appear to cause significant disruption to measured behavioral patterns.
  • Embedded sensor data holds potential for advanced animal-based behavioral monitoring in livestock management.