Tanigaki Kei, Otsuka Ryoma, Li Aiyi, Hatano Yota, Wei Yuanzhou, Koyama Shiho, Yoda Ken, Maekawa Takuya
Graduate School of Information Science and Technology, Osaka University, Suita, 565-0871 Osaka, Japan.
Graduate School of Engineering Science, Osaka University, Toyonaka, 560-8531 Osaka, Japan.
PNAS Nexus. 2024 Jan 16;3(1):pgad447. doi: 10.1093/pnasnexus/pgad447. eCollection 2024 Jan.
Rare behaviors displayed by wild animals can generate new hypotheses; however, observing such behaviors may be challenging. While recent technological advancements, such as bio-loggers, may assist in documenting rare behaviors, the limited running time of battery-powered bio-loggers is insufficient to record rare behaviors when employing high-cost sensors (e.g. video cameras). In this study, we propose an artificial intelligence (AI)-enabled bio-logger that automatically detects outlier readings from always-on low-cost sensors, e.g. accelerometers, indicative of rare behaviors in target animals, without supervision by researchers, subsequently activating high-cost sensors to record only these behaviors. We implemented an on-board outlier detector via knowledge distillation by building a lightweight outlier classifier supervised by a high-cost outlier behavior detector trained in an unsupervised manner. The efficacy of AI bio-loggers has been demonstrated on seabirds, where videos and sensor data captured by the bio-loggers have enabled the identification of some rare behaviors, facilitating analyses of their frequency, and potential factors underlying these behaviors. This approach offers a means of documenting previously overlooked rare behaviors, augmenting our understanding of animal behavior.
野生动物表现出的罕见行为能够产生新的假设；然而，观察此类行为可能具有挑战性。尽管近期的技术进步，如生物记录器，可能有助于记录罕见行为，但在使用高成本传感器（如摄像机）时，电池供电的生物记录器有限的运行时间不足以记录罕见行为。在本研究中，我们提出了一种启用人工智能（AI）的生物记录器，它能自动从始终开启的低成本传感器（如加速度计）中检测出异常读数，这些读数表明目标动物存在罕见行为，无需研究人员监督，随后激活高成本传感器仅记录这些行为。我们通过知识蒸馏实现了一个机载异常值检测器，构建了一个轻量级异常值分类器，由以无监督方式训练的高成本异常行为检测器监督。AI生物记录器的功效已在海鸟身上得到证明，生物记录器捕获的视频和传感器数据能够识别一些罕见行为，有助于分析其频率以及这些行为背后的潜在因素。这种方法提供了一种记录先前被忽视的罕见行为的手段，增进了我们对动物行为的理解。
