A benchmark for computational analysis of animal behavior, using animal-borne tags

A benchmark for computational analysis of animal behavior, using animal-borne tags

Abstract Background Animal-borne sensors (‘bio-loggers’) can record a suite of kinematic and environmental data, which are used to elucidate animal ecophysiology and improve conservation efforts. Machine learning techniques are used for interpreting the large amounts of data recorded by bio-loggers,...

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Bibliographic Details
Main Authors: Benjamin Hoffman, Maddie Cusimano, Vittorio Baglione, Daniela Canestrari, Damien Chevallier, Dominic L. DeSantis, Lorène Jeantet, Monique A. Ladds, Takuya Maekawa, Vicente Mata-Silva, Víctor Moreno-González, Anthony M. Pagano, Eva Trapote, Outi Vainio, Antti Vehkaoja, Ken Yoda, Katherine Zacarian, Ari Friedlaender
Format: Article
Language:English
Published: BMC 2024-12-01
Series:Movement Ecology
Subjects:
Machine learning
Bio-loggers
Animal behavior
Accelerometers
Time series
Self-supervised Learning
Online Access:https://doi.org/10.1186/s40462-024-00511-8
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