Automatic Identification of Stone-Handling Behaviour in Japanese Macaques Using LabGym Artificial Intelligence
This work addresses the time-intensive process of behavioral analysis for ethologists and primatologists, but it is incremental as it applies an existing AI tool to a new primate behavior without major methodological innovation.
The researchers tackled the problem of automating the analysis of stone-handling behavior in Japanese macaques using the LabGym AI tool, achieving a model with high accuracy in detection, though quantitative validation was limited due to time constraints.
The latest advancements in artificial intelligence technology have opened doors to the analysis of intricate behaviours. In light of this, ethologists are actively exploring the potential of these innovations to streamline the time-intensive process of behavioural analysis using video data. In the realm of primatology, several tools have been developed for this purpose. Nonetheless, each of these tools grapples with technical constraints that we aim to surmount. To address these limitations, we have established a comprehensive protocol designed to harness the capabilities of a cutting-edge tool, LabGym. Our primary objective was to evaluate LabGym's suitability for the analysis of primate behaviour, with a focus on Japanese macaques as our model subjects. We have successfully developed a model that demonstrates a high degree of accuracy in detecting Japanese macaques stone-handling behaviour. Our behavioural analysis model was completed as per our initial expectations and LabGym succeed to recognise stone-handling behaviour on videos. However, it is important to note that our study's ability to draw definitive conclusions regarding the quality of the behavioural analysis is hampered by the absence of quantitative data within the specified timeframe. Nevertheless, our model represents the pioneering endeavour, as far as our knowledge extends, in leveraging LabGym for the analysis of primate behaviours. It lays the groundwork for potential future research in this promising field.