ArticleCommunications biology2020
Machine learning enables improved runtime and precision for bio-loggers on seabirds.
Article in Communications biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 45 citations in OpenAlex.
- Refining Accelerometer-Based Animal Behaviour Classifications With Sequence-Informed Post-Processing.Ecology and evolution · 2026Article
- Review
- Resource-efficient data transmission for WiFi-capable bio-loggers based on machine learning.PloS one · 2026Article
- Real-Time Behaviour Recognition on Bio-Loggers Enables Autonomous Audio Playback Experiments in Free-Ranging Seabirds.Ecology and evolution · 2025Article
- A benchmark for computational analysis of animal behavior, using animal-borne tags.Movement ecology · 2024Article
- Time synchronisation for millisecond-precision on bio-loggers.Movement ecology · 2024Article
- Hidden rivals: the negative impacts of dolphinfish on seabird foraging behaviour.Biology letters · 2024Article
- Automatic recording of rare behaviors of wild animals using video bio-loggers with on-board light-weight outlier detector.PNAS nexus · 2024Article
- Mapping spatial memory in teleosts: a new Frontier in neural logging techniques.Frontiers in physiology · 2024Review
- The role of individual variability on the predictive performance of machine learning applied to large bio-logging datasets.Scientific reports · 2022Article
- A Review of Neurologgers for Extracellular Recording of Neuronal Activity in the Brain of Freely Behaving Wild Animals.Micromachines · 2022Review
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Estimation of the Maternal Investment of Sea Turtles by Automatic Identification of Nesting Behavior and Number of Eggs Laid from a Tri-Axial Accelerometer.Animals : an open access journal from MDPI · 2022Article
- Perspectives in machine learning for wildlife conservation.Nature communications · 2022Review
- Quantifying song behavior in a free-living, light-weight, mobile bird using accelerometers.Ecology and evolution · 2022Article
- R package for animal behavior classification from accelerometer data-rabc.Ecology and evolution · 2021Article
- Animal tag technology keeps coming of age: an engineering perspective.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers.Movement ecology · 2021Article
- Remote Recognition of Moving Behaviors for Captive Harbor Seals Using a Smart-Patch System via Bluetooth Communication.Micromachines · 2021Article
- Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unravelling the secrets of wild animals is one of the biggest challenges in ecology, with bio-logging (i.e., the use of animal-borne loggers or bio-loggers) playing a pivotal role in tackling this challenge. Bio-logging allows us to observe many aspects of animals' lives, including their behaviours, physiology, social interactions, and external environment. However, bio-loggers have short runtimes when collecting data from resource-intensive (high-cost) sensors. This study proposes using AI on board video-loggers in order to use low-cost sensors (e.g., accelerometers) to automatically detect and record complex target behaviours that are of interest, reserving their devices' limited resources for just those moments. We demonstrate our method on bio-loggers attached to seabirds including gulls and shearwaters, where it captured target videos with 15 times the precision of a baseline periodic-sampling method. Our work will provide motivation for more widespread adoption of AI in bio-loggers, helping us to shed light onto until now hidden aspects of animals' lives.
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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.