TY - JOUR
T1 - Machine learning enables improved runtime and precision for bio-loggers on seabirds
AU - Korpela, Joseph
AU - Suzuki, Hirokazu
AU - Matsumoto, Sakiko
AU - Mizutani, Yuichi
AU - Samejima, Masaki
AU - Maekawa, Takuya
AU - Nakai, Junichi
AU - Yoda, Ken
N1 - Funding Information:
We thank Rory P. Wilson, Flavio Quintana, Agustina Gómez Laich, Takashi Yamamoto, Yasue Kishino, and Kazuya Murao for suggestions and comments on this work. This study is partially supported by JSPS KAKENHI JP16H06539, JP16H06541 and JP16H06536.
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - Unravelling the secrets of wild animals is one of the biggest challenges in ecology, with bio-logging (i.e., the use of animal-borne loggers or bio-loggers) playing a pivotal role in tackling this challenge. Bio-logging allows us to observe many aspects of animals’ lives, including their behaviours, physiology, social interactions, and external environment. However, bio-loggers have short runtimes when collecting data from resource-intensive (high-cost) sensors. This study proposes using AI on board video-loggers in order to use low-cost sensors (e.g., accelerometers) to automatically detect and record complex target behaviours that are of interest, reserving their devices’ limited resources for just those moments. We demonstrate our method on bio-loggers attached to seabirds including gulls and shearwaters, where it captured target videos with 15 times the precision of a baseline periodic-sampling method. Our work will provide motivation for more widespread adoption of AI in bio-loggers, helping us to shed light onto until now hidden aspects of animals’ lives.
AB - Unravelling the secrets of wild animals is one of the biggest challenges in ecology, with bio-logging (i.e., the use of animal-borne loggers or bio-loggers) playing a pivotal role in tackling this challenge. Bio-logging allows us to observe many aspects of animals’ lives, including their behaviours, physiology, social interactions, and external environment. However, bio-loggers have short runtimes when collecting data from resource-intensive (high-cost) sensors. This study proposes using AI on board video-loggers in order to use low-cost sensors (e.g., accelerometers) to automatically detect and record complex target behaviours that are of interest, reserving their devices’ limited resources for just those moments. We demonstrate our method on bio-loggers attached to seabirds including gulls and shearwaters, where it captured target videos with 15 times the precision of a baseline periodic-sampling method. Our work will provide motivation for more widespread adoption of AI in bio-loggers, helping us to shed light onto until now hidden aspects of animals’ lives.
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U2 - 10.1038/s42003-020-01356-8
DO - 10.1038/s42003-020-01356-8
M3 - Article
C2 - 33127951
AN - SCOPUS:85094639295
VL - 3
JO - Communications Biology
JF - Communications Biology
SN - 2399-3642
IS - 1
M1 - 633
ER -