Tiny AI-based bio-loggers revealing the interesting bits of a bird’s day
Researchers from Osaka University find a way that, without supervision by researchers, automatically captures video of rare animal behavior over the long term in extreme environments using a light-weight AI controller to preserve bio-logger battery power
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FINDING AND RECORDING RARE BEHAVIORS OF WILDLIFE BY AI
view moreCREDIT: TAKUYA MAEKAWA, OSAKA UNIVERSITY
Osaka, Japan – Have you ever wondered what wildlife animals do all day? Documentaries offer a glimpse into their lives, but animals under the watchful eye do not do anything interesting. The true essence of their behaviors remains elusive. Now, researchers from Japan have developed a camera that allows us to capture these behaviors.
In a study recently published in PNAS Nexus, researchers from Osaka University have created a small sensor-based data logger (called a bio-logger) that automatically detects and records video of infrequent behaviors in wild seabirds without supervision by researchers.
Infrequent behaviors, such as diving into the water for food, can lead to new insights or even new directions in research. But observing enough of these behaviors to infer any results is difficult, especially when these behaviors take place in an environment that is not hospitable to humans, such as the open ocean. As a result, the detailed behaviors of these animals remain largely unknown.
“Video cameras attached to the animal are an excellent way to observe behavior,” says Kei Tanigaki, lead author of the study. However, video cameras are very power hungry, and this leads to a trade-off. “Either the video only records until the battery runs out, in which case you might miss the rare behavior, or you use a larger, heavier battery, which is not suitable for the animal.”
To avoid having to make this choice for the wild seabirds under study, the team use low-power sensors, such as accelerometers, to determine when an unusual behavior is taking place. The camera is then turned on, the behavior is recorded, and the camera powers off until the next time. This bio-logger is the first to use artificial intelligence to do this task.
“We use a method called an isolation forest,” says Takuya Maekawa, senior author. “This method detects outlier events well, but like many other artificial intelligence algorithms, it is computationally complex. This means, like the video cameras, it is power hungry.” For the bio-loggers, the researchers needed a light-weight algorithm, so they trained the original isolation forest on their data and then used it as a “teacher” to train a smaller “student” outlier detector installed on the bio-logger.
The final bio-logger is 23 g, which is less than 5% of the body weight of the Streaked Shearwater birds under study. Eighteen bio-loggers were deployed, a total of 205 hours of low-power sensor data were collected, and 76 5-min videos were collected. The researchers were able to collect enough data to reveal novel aspects of head-shaking and foraging behaviors of the birds.
This approach, which overcomes the battery-life limitation of most bio-loggers, will help us understand the behaviors of wildlife that venture into human-inhabited areas. It will also enable animals in extreme environments inaccessible to humans to be observed. This means that many other rare behaviors — from sweet-potato washing by Japanese monkeys to penguins feeding on jellyfish — can now be studied in the future.
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The article, “Automatic recording of rare behaviors of wild animals using video bio-loggers with on-board light-weight outlier detector,” was published in PNAS Nexus at DOI: 10.1093/pnasnexus/pgad447
AI-enabled device and videos automatically recorded by the device
CREDIT
Takuya Maekawa, Osaka University
Introduction of AI-enabled bio-logger for finding rare behaviors (VIDEO)
About Osaka University
Osaka University was founded in 1931 as one of the seven imperial universities of Japan and is now one of Japan's leading comprehensive universities with a broad disciplinary spectrum. This strength is coupled with a singular drive for innovation that extends throughout the scientific process, from fundamental research to the creation of applied technology with positive economic impacts. Its commitment to innovation has been recognized in Japan and around the world, being named Japan's most innovative university in 2015 (Reuters 2015 Top 100) and one of the most innovative institutions in the world in 2017 (Innovative Universities and the Nature Index Innovation 2017). Now, Osaka University is leveraging its role as a Designated National University Corporation selected by the Ministry of Education, Culture, Sports, Science and Technology to contribute to innovation for human welfare, sustainable development of society, and social transformation.
Website: https://resou.osaka-u.ac.jp/e
JOURNAL
PNAS Nexus
METHOD OF RESEARCH
Computational simulation/modeling
SUBJECT OF RESEARCH
Animals
ARTICLE TITLE
Automatic recording of rare behaviors of wild animals using video bio-loggers with on-board light-weight outlier detector
ARTICLE PUBLICATION DATE
16-Jan-2024
AI naturalists record rare bird behavior
VIDEO:
RARE BEHAVIORS RECORDED BY AI-ENABLED BIO-LOGGERS AFFIXED TO STREAKED SHEARWATERS.
view moreCREDIT: TANIGAKI ET AL, OSAKA UNIVERSITY
For centuries, naturalists have braved trackless forests, windy clifftops, and the cramped confines of blinds and submarines, hoping to capture rare behaviors that might reveal important aspects of animal biology and ecology. Takuya Maekawa and colleagues sought to deploy wearable trackers, which have become common in animal biology, to capture rare behaviors for study. As animal-borne video loggers can only capture a few hours of video due to battery limitations, a key challenge is deciding when to record. The authors created an on-device AI program capable of “unsupervised learning” to automatically find and record rare behaviors without supervision by human naturalists. First, an outlier detector program was trained on unlabeled accelerometer and water-depth data from seabirds to automatically determine when an unusual behavior is taking place. This outlier detector program was used to create streamlined outlier detectors—one for accelerometer data and one for water-depth data—that fit on a low-energy micro control unit on a logger with limited memory and computational power. These detectors turn on a video camera of the logger when a rare behavior occurs in real time. The final AI-enabled bio-logger includes a video camera, three-axis acceleration sensor, GPS unit, water pressure sensor, thermometer, magnetometer, and illuminometer, which was then affixed to a streaked shearwater (Calonectris leucomelas). The bio-logger weighs 23 g, less than 5% the weight of a shearwater. In field trials in 2022, the authors attached the bio-loggers to 18 birds. The acceleration-based rare-behavior detectors recorded videos of vigorous head shaking near the beginning of flight that the authors hypothesize may function to remove nasal salt gland fluids and other external materials to increase subsequent flight efficiency. The depth-based rare behavior detectors captured 50 minutes of active foraging for fish—including preliminary below-water peeks before diving—behavior rarely caught on camera. According to the authors, AI-enabled bio-loggers can be used on a range of species to capture many kinds of seldom-seen moments, including deep-sea mating rituals, the hunting strategies used for rare prey items, and the causes of death of wild animals.
JOURNAL
PNAS Nexus
ARTICLE TITLE
Automatic recording of rare behaviors of wild animals using video bio-loggers with on-board light-weight outlier detector
ARTICLE PUBLICATION DATE
16-Jan-2024
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