We develop AI technologies for the automatic analysis of human and animal behavioral data, enabling data mining approaches that uncover previously undiscovered knowledge. Our research has wide-ranging real-world applications, including: analyzing the behavior of healthy and disease-model animals to support drug discovery; evaluating the impact of environmental changes on wildlife behavior; assessing the effects of wildlife damage control policies on animal behavior; extracting tacit knowledge and expertise from skilled workers’ operational data. Through these efforts, we aim to transform behavioral data into actionable insights for science, industry, and society.
Takuya Maekawa, Daiki Higashide, Takahiro Hara, Kentarou Matsumura, Kaoru Ide, Takahisa Miyatake, Koutarou D Kimura, Susumu Takahashi
Nature Communications, (Sep. 2021)
We have developed a method for cross-species behavior analysis using attention-based domain-adversarial deep neural networks.
Takuya Maekawa, Kazuya Ohara, Yizhe Zhang, Matasaburo Fukutomi, Sakiko Matsumoto, Kentarou Matsumura, Hisashi Shidara, Shuhei J. Yamazaki, Ryusuke Fujisawa, Kaoru Ide, Naohisa Nagaya, Koji Yamazaki, Shinsuke Koike, Takahisa Miyatake, Koutarou D. Kimura, Hiroto Ogawa, Susumu Takahashi, Ken Yoda
Nature Communications, (Oct. 2020)
We propose a deep learning-assisted comparative analysis method of animal trajectories named DeepHL.
