Nonlinear Methods for Analyzing Pose in Behavioral Research

ArXi:2604.01453v1 Announce Type: new Advances in markerless pose estimation have made it possible to capture detailed human movement in naturalistic settings using standard video, enabling new forms of behavioral analysis at scale. However, the high dimensionality, noise, and temporal complexity of pose data raise significant challenges for extracting meaningful patterns of coordination and behavioral change. This paper presents a general-purpose analysis pipeline for human pose data, designed to both linear and nonlinear characterizations of movement across diverse experimental contexts.