Nonlinear Methods for Analyzing Pose in Behavioral Research

TL;DR

This paper introduces a versatile analysis pipeline for complex pose data, combining preprocessing, dimensionality reduction, and recurrence analysis to extract meaningful movement patterns across various behavioral research contexts.

Contribution

It presents a general-purpose, adaptable analysis workflow for high-dimensional, noisy pose data, supporting both linear and nonlinear movement characterizations.

Findings

The pipeline effectively captures temporal structure in diverse pose datasets.

Case studies demonstrate the method's flexibility across different movement types.

The approach enables meaningful behavioral insights from complex pose time series.

Abstract

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 support both linear and nonlinear characterizations of movement across diverse experimental contexts. The pipeline combines principled preprocessing, dimensionality reduction, and recurrence-based time series analysis to quantify the temporal structure of movement dynamics. To illustrate the pipeline's flexibility, we present three case studies spanning facial and full-body movement, 2D and 3D data, and individual versus multi-agent behavior. Together, these examples demonstrate how the same analytic workflow can be adapted to extract theoretically meaningful insights from complex pose time series.