Brief

Researchers have developed Quantitative Movement Testing (QMT), a computer vision pipeline that extracts 3D kinematic biomarkers from standard smartphone videos. This method uses deep learning-based 3D pose estimation to offer a more accessible and objective alternative to costly laboratory-based motion capture systems. QMT has demonstrated high agreement with gold-standard methods and shows promise for tracking disease progression and treatment response in clinical trials, particularly for chronic pain conditions. AI

Researchers have developed a novel method called Complex Hilbert Principal Component Analysis (CHPCA) to analyze whole-body coordination in sports motion using markerless 3D pose estimation data. This technique segments motion phases automatically and extends analysis to body surface mesh vertices, representing kinematic chains as continuous phase fields. The framework reveals a trunk-anchored global phase architecture and quantifies functional asymmetries between preparation and execution phases, bridging kinematic and kinetic descriptions of movement. AI