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

Menaker, Tom, Zamansky, Anna, van der Linden, Dirk, Kaplun, Dmitry, Sinitica, Aleksandr, Karl, Sabrina and Huber, Ludwig (2020) Towards a Methodology for Data-Driven Automatic Analysis of Animal Behavioral Patterns. In: Proceedings of the Seventh International Conference on Animal-Computer Interaction. ACM, New York, NY, pp. 1-6. ISBN 10.1145/3446002.3446126

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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.

Item Type: Book Section
Uncontrolled Keywords: Animal-Computer Interaction, data mining, Ethology
Subjects: G400 Computer Science
G500 Information Systems
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 29 Mar 2021 09:35
Last Modified: 31 Jul 2021 15:34

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