Life science
Smart devices, Behavior analysis

Machine learning techniques for understanding
animal behaviors
Graduate School of Information Science and Technology
Associate Professor Takuya Maekawa
Researchmap https://researchmap.jp/takumae80/

Life science

AbstractSensing and analyzing animal behaviors can be a promising
method for understanding the animals. In this study, we have developed state-of-the-art sensing and analysis methods for animal
locomotion based on machine learning. It was difficult for existing
camera-enabled animal-borne biologging devices for small animals to always record videos due to the limitation of the weight of
their batteries, making it difficult to capture biologically importantand infrequent events. Our research group developed AI-enabledbiologging devices that automatically detect behaviors of animals
using low-energy sensors, permitting to record videos only when animportant event occurs (e.g., foraging). In addition, we have developed a deep learning method for supporting analysis of huge animal locomotion data. We focus on comparative analysis of animal
trajectories (e.g., healthy vs. disease mouse groups) and develop
a method that automatically detects sub-trajectories characteristicof a group. Thanks to the proposed method, we could discover
several new biological findings such as a feature of the Parkinson’s
disease mice regarding frequency of exploring surroundings and asurvival strategy of insects that perform “death-feigning”.

▍ Background & Results
Animal-borne biologging devices enable us to observe animal
behaviors that are difficult for biologists to directly observe. However, the limitation of the battery size of a logging device attached tosmall animals makes it difficult to record biologically important andinfrequent animal behaviors. Our research group developed AI-enabled biologging devices that automatically recognizes behaviors
of animals using low-energy sensors such as GPS and acceleration sensors, permitting to record videos only when an important
event occurs (e.g., foraging). Because the computation power ofa micro-controller on a small and poor logging device is limited,
our research group developed an activity recognition method thatis computationally efficient and high accuracy. In an experiment of
black-tailed gulls, we could successfully capture foraging eventswith 15 times the precision of a baseline periodic-sampling method. Thanks to our method, we could record new foraging behaviorsof the birds (Fig. 1).
Animal trajectory data observed by sensing devices such as
GPS have been analyzed by biologists to, for example, evaluate
effects of diseases on animal behaviors by comparing healthy anddisease animal groups and reveal the difference in survival strategies between male and female groups. However, existing analysis
methods have relied on manual analysis by biologist, placing hugeburdens on the biologists. Therefore, our research group developed a deep learning-aided method that automatically detects
characteristic sub-trajectories to a group in comparative analysis.
The proposed network model with the attention mechanism enabled us to discover new findings from various animals such as seabirds, bears, worms, mice, and insects (Fig. 2).

▍ Significance of the research and Future perspective
These new sensing and analysis methods can accelerate understanding of zoonotic infection and symbiosis with wild animals.

Developed logger

4xKleptoparasitism by black-tailed gull

Capturing flying insect while flying

Capturing fish by diving

Capturing insect on the sea while flying

Fig.1

1,898

Start-2,048

Start-2,562-2,502-1,992

Trajectory of beetle with long
duration of death-feigning

Red flour beetle

1,086
1,357(mm)1,766Trajectory of beetle with shortduration of death-feigning

StartStart

Mouse
Trajectories of healthy miceFig.2 (Photos courtesy of Okayama University and Doshisha University)
Fig.3 (Photos courtesy of Nagoya University

Trajectory of female seabird

Trajectory of male seabird

Seabird (streakedshearwater)

Fig.3 (Photos courtesy of Nagoya University)

P a t e n t
Maekawa, Takuya et al. Deep Learning-assisted Comparative Analysis of Animal Trajectories with DeepHL. Nature Communications. 2020;11:5316. doi: 10.
1038/s41467-020-19105-0
Korpela, Joseph; Maekawa, Takuya et al. Machine learning enables improved runtime and precision for bio-loggers on seabirds. Communications Biology.
2020;3:633. doi:10.1038/s42003-020-01356-8
UR
L https://www.youtube.com/channel/UCHoNDVk6sBOt4d8A2jralEg
K e y w o r d biologging, machine learning, activity recognition
Tr e a t i s e