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

You might also read

Related Articles

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

Sort by
Same author

Emergence of functional prey depletion halo through penguin-krill behavioural dynamics.

Proceedings. Biological sciences·2026
Same author

Unsuccessful foragers acquire social information through group departure and travel in penguins.

Proceedings. Biological sciences·2026
Same author

Contemporary contamination and risk of persistent organic pollutants in sediments of an urbanised estuarine and coastal ecosystem.

Marine pollution bulletin·2026
Same author

Association of Lipid Core Burden Index With Early Progression of Cardiac Allograft Vasculopathy in Patients After Heart Transplantation.

Circulation journal : official journal of the Japanese Circulation Society·2026
Same author

Transient changes in body weight and behavior during the placentation period in non-human primates and rodents.

Scientific reports·2026
Same author

Evidence for latitude-driven changes in diel rhythms in a wide-ranging seabird.

Proceedings. Biological sciences·2026

Related Experiment Video

Updated: Jun 23, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Can ethograms be automatically generated using body acceleration data from free-ranging birds?

Kentaro Q Sakamoto1, Katsufumi Sato, Mayumi Ishizuka

  • 1Graduate School of Veterinary Medicine, Hokkaido University, Sapporo, Hokkaido, Japan. sakamoto@vetmed.hokudai.ac.jp

Plos One
|May 1, 2009
PubMed
Summary

This study introduces "Ethographer," an automated method using accelerometers to classify animal behavior from motion data. This technique accurately categorizes behaviors like diving and flight in seabirds without prior knowledge.

More Related Videos

Implantation of Radiotelemetry Transmitters Yielding Data on ECG, Heart Rate, Core Body Temperature and Activity in Free-moving Laboratory Mice
09:11

Implantation of Radiotelemetry Transmitters Yielding Data on ECG, Heart Rate, Core Body Temperature and Activity in Free-moving Laboratory Mice

Published on: November 21, 2011

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Related Experiment Videos

Last Updated: Jun 23, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Implantation of Radiotelemetry Transmitters Yielding Data on ECG, Heart Rate, Core Body Temperature and Activity in Free-moving Laboratory Mice
09:11

Implantation of Radiotelemetry Transmitters Yielding Data on ECG, Heart Rate, Core Body Temperature and Activity in Free-moving Laboratory Mice

Published on: November 21, 2011

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Area of Science:

  • Animal behavior analysis
  • Bio-logging technology
  • Ecological monitoring

Background:

  • Traditional animal behavior studies rely on direct observation, which is challenging for remote or deep-sea activities.
  • Animal-borne data loggers, especially accelerometers, offer a solution by recording dynamic motion.
  • Classifying behaviors from acceleration data often requires pre-existing knowledge of animal movements.

Purpose of the Study:

  • To develop and evaluate an automated procedure for categorizing animal behavior using body acceleration data.
  • To introduce a user-friendly computer application, "Ethographer," for this purpose.
  • To overcome limitations of direct observation in studying animal behavior in challenging environments.

Main Methods:

  • Utilized longitudinal acceleration data from European shags (Phalacrocorax aristotelis).
  • Applied continuous wavelet transformation to convert time-series data into spectra.
  • Employed unsupervised k-means cluster analysis to categorize spectral data into 20 behavior groups.
  • Validated classifications against independent depth profile data.

Main Results:

  • The automated procedure successfully categorized behaviors based on body acceleration patterns.
  • Classified behaviors, such as being on land, in flight, or diving, showed strong agreement with depth profiles.
  • The method identified characteristic periodicities in body acceleration corresponding to specific behaviors.
  • The unsupervised approach has the potential to discover novel behaviors and sequences.

Conclusions:

  • The "Ethographer" application and automated procedure provide a robust method for classifying animal behavior from accelerometer data.
  • This approach overcomes the limitations of direct observation for studying animals in remote or extreme environments.
  • The unsupervised nature of the analysis allows for the potential discovery of previously unknown behaviors and behavioral patterns.