Agreement of markerless and marker-based joint kinetics in team sport-specific change-of-direction movements

Abstract

Abstract Markerless (ML) motion capture is a practical alternative to marker-based (MB) systems, yet its ability to estimate joint kinetics during team sport-specific movements remains unclear. This study evaluated the agreement between ML and MB lower extremity joint moment estimates during change-of-direction (COD) tasks. Nineteen team sport athletes performed straight running and four COD movements (45-, 90-, 135-, 180-degrees) at different speeds. Joint moments at the hip, knee, and ankle were computed from synchronized force-plate data and either ML video data (Theia3D) or MB motion-capture data. Agreement was assessed using an extended Bland–Altman approach with linear mixed-effects models for peak joint moments and bootstrapped functional prediction bands for the entire stance phase. Substantial random and systematic differences were observed between systems exceeding the defined threshold of ≤ 10% in relative limits of agreement (LoAs) and in average MB signal range × 100% stance. LoAs for peak joint moments ranged from ± 0.38 Nm/kg at the ankle to ± 1.21 Nm/kg at the hip, indicating that difference between systems was of similar magnitude to, or larger than, the underlying MB-derived peak moments. Relative random differences ranged from 92 to 128% across movement planes. Agreement did not show a consistent pattern across movement directions or direction-specific speed categories. These findings indicate that ML- and MB-derived joint kinetics cannot be used interchangeably.