用非线性方法分析自然场景下人体姿态，挖掘行为模式与动态结构。
Nonlinear Methods for Analyzing Pose in Behavioral Research
- 融合预处理、降维与递归分析，统一处理高维姿态数据
- 在面部与全身动作中均能识别出稳定的运动动态结构
- 适用于个体与多人互动的复杂行为研究，通用性强
基于无标记姿态估计技术的进步，现可通过普通视频在自然环境中捕捉详细的人体运动，推动大规模行为分析的发展。然而，姿态数据具有高维度、噪声大和时间复杂等特点，给提取协调模式与行为变化的有意义规律带来挑战。本文提出一个通用分析流程，支持在多种实验情境中对人类姿态数据进行线性与非线性表征。该流程结合严谨的预处理、降维与基于递归的时间序列分析，量化运动动态的时序结构。为展示其灵活性，本文包含三个案例研究：涵盖面部与全身动作、2D与3D数据，以及个体与多主体行为。这些案例共同表明，同一分析流程可适配不同场景，从复杂姿态时序中提取理论上有意义的洞见。
原文摘要 · Abstract (English)
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.
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