Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface

2026-04-27

AI Summary

Current analyses of movement coordination are often limited to adjacent joints or rely on marker-based systems, lacking a unified representation framework applicable to full-body, markerless data. This work proposes a fully automatic, prior-free phase segmentation method based on markerless 3D pose estimation. By employing Complex Hilbert Principal Component Analysis (CHPCA), the approach extracts dominant whole-body phase patterns and extends them onto body surface meshes to construct a continuous phase field that characterizes intersegmental coordination. Applied to hammering actions, the method reveals a trunk-anchored global phase architecture and quantifies functional asymmetry between preparation and execution phases. Phase reorganization is highly significant across 1,079 surface vertices (p < 10⁻¹⁰), and the primary phase mode strongly correlates with kinetic energy mobilization during the swing phase (ρ ≈ 0.71).

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Quantitative analysis of the kinematic chain in sports motion is essential for performance evaluation and injury prevention. Conventional methods such as the kinematic-sequence (KS) and continuous relative phase (CRP) are confined to adjacent joint pairs and lack a unified framework for whole-body coordination, while segmental power-flow analysis requires force plates and inertial parameters that restrict it to laboratory environments. We apply Complex Hilbert Principal Component Analysis (CHPCA) separately to each motion phase (backswing and downswing) on markerless 3D pose estimation data, extracting the dominant whole-body phase pattern as a single complex eigenvector. The pipeline further includes a fully automatic signal-based phase segmentation (no priors on strike count or rest location) and an extension to 1,079 body-surface mesh vertices, so that the kinematic chain is represented as a continuous phase field across the body. On 14 hammer-striking trials of a single subject, the framework reveals (i) a trunk-anchored global phase architecture, (ii) a functional asymmetry between preparation and execution phases quantified by Mode-1 contribution (45.5% vs. 70.5%) and inter-trial Spearman consistency (0.38 vs. 0.58), and (iii) a consistent reorganisation across both skeletal joints and mesh vertices ($p < 10^{-10}$ on 1,079 vertices). As a methodological consistency check, pairwise phase differences from the Mode-1 eigenvector are compared against CRP on all 190 joint pairs by a permutation test ($ρ= 0.473$, $p = 0.0005$). A correspondence analysis between Mode-1 amplitude and kinetic-energy mobilisation variance further shows a strong positive correlation in the downswing ($ρ\approx 0.71$ on both skeleton and mesh) and no correlation in the backswing, indicating that the proposed framework bridges kinematic and kinetic descriptions of coordination through phase structure.

Problem

• whole-body coordination
• markerless 3D pose estimation
• kinematic chain
• phase analysis
• sports motion

Innovation

• Complex Hilbert PCA
• phase-separated analysis
• markerless 3D pose estimation
• continuous phase field
• whole-body coordination