Towards a Methodology for Data-Driven Automatic Analysis of Animal Behavioral Patterns

Tom Menaker, Anna Zamansky, Dirk Van Der Linden, Dmitry Kaplun, Aleksandr Sinitica, Sabrina Karl, Ludwig Huber

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Measurement of behavior a major challenge in many animal-related disciplines, including ACI. This usually requires choosing specific parameters for measuring, related to the investigated hypothesis. Therefore, a key challenge is determining a priori what parameters are informational for a given experiment. The scope of this challenge is raised even further by the emerging computational approaches for animal detection and tracking, as automatizing behavioral measurement makes the possibilities for measuring behavioral parameters practically endless. This paper approaches these challenges by proposing a framework for guiding the decision making of researchers in their future data analysis. The framework is data-driven in the sense that it applies data mining techniques for obtaining insights from experimental data for guiding the choice of certain behavioral parameters. Here, we demonstrate the approach using a concrete example of clustering-based analysis of trajectories which can identify 'prevalent areas of stay' of the animal subjects in the experimental setting.

Original languageEnglish
Title of host publicationACI 2020
Subtitle of host publicationEmbodied Dialogues - 7th International Conference on Animal-Computer Interaction, Proceedings
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450375740
DOIs
StatePublished - 10 Nov 2020
Event7th International Conference on Animal-Computer Interaction: Embodied Dialogues, ACI 2020 - Virtual, Online, United Kingdom
Duration: 10 Nov 202012 Nov 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference7th International Conference on Animal-Computer Interaction: Embodied Dialogues, ACI 2020
Country/TerritoryUnited Kingdom
CityVirtual, Online
Period10/11/2012/11/20

Bibliographical note

Funding Information:
The research was supported by the grant from the Ministry of Science and Technology of Israel and RFBR according to the research project no. 19-57-06007.

Publisher Copyright:
© 2020 ACM.

Keywords

  • animal data mining
  • computational ethology

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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