Machine learning enables improved runtime and precision for bio-loggers on seabirds

• Published in: Communications biology 2020-10, Vol.3 (1), p.633-633, Article 633
• Main Authors: Korpela, Joseph, Suzuki, Hirokazu, Matsumoto, Sakiko, Mizutani, Yuichi, Samejima, Masaki, Maekawa, Takuya, Nakai, Junichi, Yoda, Ken
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/158/856; Animals; Biology; Biomedical and Life Sciences; Learning algorithms; Life Sciences; Machine learning; Motivation; Social interactions
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-020-01356-8

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. Joseph Korpela et al. demonstrate the use of machine-learning assisted bio-loggers on black-tailed gulls and streaked shearwaters. As video recording is only activated through variations in movement detected by low-cost accelerometers, this method represents improvements to runtime and precision over existing bio-logging technology.