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

Tom Menaker, Anna Zamansky, Dirk van der Linden, Dmitry Kaplun, Aleksandr Sinitca, 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: Proceedings of the Seventh International Conference on Animal-Computer Interaction
Place of PublicationNew York, NY
PublisherACM
Number of pages6
ISBN (Electronic)9781450375740
DOIs
Publication statusPublished - 10 Nov 2020

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