Dataset for Automatic Identification of Stone-Handling Behaviour in Japanese Macaques Using LabGym Artificial Intelligence

Dataset for Automatic Identification of Stone-Handling Behaviour in Japanese Macaques Using LabGym Artificial Intelligence
Abstract: The latest advances in artificial intelligence technology have opened doors to the video analysis of complex behaviours. In light of this, ethologists are actively exploring the potential of these innovations to streamline the time-intensive behavioural analysis process using video data. Several tools have been developed for this purpose in primatology in the past decade. Nonetheless, each tool grapples with technical constraints. To address these limitations, we have established a comprehensive protocol designed to harness the capabilities of a cutting-edge tool, LabGym. The primary objective of this study was to evaluate LabGym's suitability for the analysis of primate behaviour, focusing on Japanese macaques as our model subjects. First, we developed a model that accurately detects Japanese macaques, allowing us to analyse their actions using LabGym. Our behavioural analysis model succeeded in recognising stone-handling-like behaviours on video. However, the absence of quantitative data within the specified timeframe limits our study's ability to draw definitive conclusions regarding the quality of the behavioural analysis. Nevertheless, as far as our knowledge extends, our model represents the pioneering endeavour in leveraging LabGym to analyse primate behaviours. It lays the groundwork for future research in this promising field to complexify our model using LabGym's latest version and associated tools such as multiclass detection and interactive behaviour analysis.
Keywords: Artificial Intelligence, Ethology, Primate Behaviour, Deep learning, Japanese Macaques