Nonlinear Methods for Analyzing Pose in Behavioral Research

2026-04-01

AI Summary

This work addresses the challenge of uncovering intrinsic patterns underlying behavioral coordination and dynamic change in high-dimensional, noisy, and temporally complex human pose data. To this end, the authors propose a general-purpose analytical framework that integrates systematic preprocessing, flexible dimensionality reduction—accommodating both linear and nonlinear methods—and temporal recurrence analysis. The framework is designed to handle diverse pose data modalities, including facial or full-body, 2D or 3D, and single- or multi-person configurations. It enables unified modeling of pose dynamics across varied experimental contexts and demonstrates strong effectiveness and generalizability in extracting theoretically interpretable movement patterns, as validated through three empirical case studies.

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.

Problem

• pose estimation
• high dimensionality
• temporal complexity
• behavioral analysis
• movement dynamics

Innovation

• pose estimation
• nonlinear dynamics
• recurrence analysis
• dimensionality reduction
• behavioral analysis