Automated analysis of animal behavior
Date:
April 25, 2022
Source:
ETH Zurich
Summary:
Researchershave developed a new method that uses artificial
intelligence to analyze animal behavior. This opens the door to
longer-term in-depth studies in the field of behavioral science --
while also helping to improve animal welfare.
FULL STORY ========================================================================== Researchers engaged in animal behaviour studies often rely on hours
upon hours of video footage which they manually analyse. Usually, this
requires researchers to work their way through recordings spanning several weeks or months, laboriously noting down observations on the animals' behaviour. Now researchers at ETH Zurich and University of Zurich have
come up with an automated way to analyse these kinds of recordings. The image-analysis algorithm they have developed makes use of computer
vision and machine learning. It can distinguish individual animals and
identify specific behaviours, such as those that signal curiosity, fear,
or harmonious social interactions with other members of their species.
==========================================================================
The technology essentially offers scientists a one-click solution for automatically analysing video footage, however lengthy or detailed
the recordings are. Another advantage of the new method is its
reproducibility: if different groups of researchers use the same
algorithm to analyse their video data, comparing results is easier
because everything is based on the same standards. What's more, the new algorithm is so sensitive that it can even identify subtle behavioural
changes that develop very gradually over long periods of time. "Those are
the kinds of changes that are often tricky to spot with the human eye,"
says Markus Marks, lead author of the research study and a postdoc in
the group headed by Professor of Neurotechnology Mehmet Fatih Yanik.
Suitable for all animal species The researchers trained the
machine-learning algorithm with video footage of mice and macaques in captivity. However, they stress that the method can be applied to all
animal species. News of their new method has already spread through
the scientific community. The ETH researchers have made the algorithm
available to other researchers on a public platform, and many of their colleagues around the world are already using it. "Interest has been particularly high among primate researchers, and our technology is already being used by a group that is researching wild chimpanzees in Uganda,"
Marks says.
This is probably because the method can also be used to analyse complex
social interactions in animal communities, such as identifying which
animals groom other members of their group and how often this occurs. "Our method offers some major advantages over previous machine-learning-based behavioural analysis algorithms, especially when it comes to analysing
social behaviour in complex settings," Marks says.
Improving conditions for animals in human care The new method can also be
used to improve animal husbandry, enabling round- the-clock monitoring
to automatically single-out abnormal behaviours. By detecting adverse
social interactions or the onset of disease early on, keepers can swiftly respond to improve conditionss for the animals in their care.
The ETH researchers are also currently collaborating with Zurich Zoo,
which wants to further improve its animal husbandry and conduct automated behavioural research. For example, in a recently published study examining patterns of elephant sleep behaviour, zoo researchers had to manually
annotate nocturnal video recordings. Their hope is that the new method
would enable them to automate and upscale such findings in the future.
Finally, the method is used in fundamental research in the fields of
biology, neurobiology and medicine. "Our method can recognise even subtle
or rare behavioural changes in research animals, such as signs of stress, anxiety or discomfort," says Yanik. "Therefore, it can not only help
to improve the quality of animal studies but also helps to reduce the
number of animals and the strain on them." The ETH Zurich professor
is planning to use the method himself as part of his neurobiological
research in the field of imitation learning.
========================================================================== Story Source: Materials provided by ETH_Zurich. Original written by
Fabio Bergamin. Note: Content may be edited for style and length.
========================================================================== Journal Reference:
1. Markus Marks, Qiuhan Jin, Oliver Sturman, Lukas von Ziegler, Sepp
Kollmorgen, Wolfger von der Behrens, Valerio Mante,
Johannes Bohacek, Mehmet Fatih Yanik. Deep-learning-based
identification, tracking, pose estimation and behaviour
classification of interacting primates and mice in complex
environments. Nature Machine Intelligence, 2022; 4 (4): 331 DOI:
10.1038/s42256-022-00477-5 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/04/220425104855.htm
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