An Open-Source Tool for Automated Human-Level Circling Behavior Detection.

Quantifying behavior and relating it to underlying biological states is of paramount importance in many life science fields. Although barriers to recording postural data have been reduced by progress in deep-learning-based computer vision tools for keypoint tracking, extracting specific behaviors from this data remains challenging. Manual behavior coding, the present gold standard, is labor-intensive and subject to intra- and inter-observer variability. Automatic methods are stymied by the difficulty of explicitly defining complex behaviors, even ones which appear obvious to the human eye. Here, we demonstrate an effective technique for detecting one such behavior, a form of locomotion characterized by stereotyped spinning, termed 'circling'. Though circling has an extensive history as a behavioral marker, at present there exists no standard automated detection method. Accordingly, we developed a technique to identify instances of the behavior by applying simple postprocessing to markerless keypoint data from videos of freely-exploring (