Online computational ethology based on modern IT infrastructure

Leon B. Larsen*, Mathias M. Neerup, John Hallam

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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In the study of animal behaviour, annotation and analysis is largely done manually either directly in the field or from recordings. An emerging field, computational ethology, is challenging this approach by using machine learning to automate the process. However, the use of such methods in general is complicated by a lack of modularity, leading to high cost and long development times. At the same time, the benefits of implementing a fully automated pipeline are often minuscule. We propose online analysis as a way to gain more from automating the process, such as making it easier to ensure that equipment is properly configured and calibrated, enabling the recording equipment to follow the animals, and even enabling closed-loop experiments. In this work, we discuss the requirements and challenges for such a system and propose an implementation based on modern IT infrastructure. Finally, we demonstrate the system in case studies of bats and mongoose. As more and more methods and algorithms are developed we expect online systems to enable new experimental setups to study behaviour, leading to new insights in the field.

Original languageEnglish
Article number101290
JournalEcological Informatics
Number of pages10
Publication statusPublished - Jul 2021


  • Automated annotation
  • Bioacoustics
  • Recording


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