Joint Angle Trajectories Are Robust to Segment Length Estimation Methods in Human Reaching.

Background: Quantitative movement analysis is increasingly used to assess motor deficits, but joint angle calculations depend on assumptions about limb segment lengths. These lengths are often estimated from average anthropometric proportions rather than measured directly. The extent to which such assumptions influence joint angle accuracy and variability remains unclear. Methods: In prior studies, we recorded reaching movements in nine healthy adults using active-marker motion capture system. In this study, we computed arm joint angles with a dynamic model scaled using either measured segment lengths (Individual method) or proportions based on body height (Average method). We compared segment proportions and the variability in joint angle trajectories arising from segment length assumptions (between-subject variability) with within-subject variability across repeated movements. Results: Segment length proportions remained unchanged despite increases in population height. Joint angle trajectories derived from the two scaling methods were very similar. Segment length assumptions had only minor effects on joint angle amplitudes, primarily due to kinematic redundancy, and these effects were substantially smaller than the within-subject variability observed across repeated movements in most individuals. Importantly, while segment length estimates shifted absolute joint angle amplitudes, they did not alter the shape of angular trajectories. Conclusions/Significance: Morphological variability in segment lengths contributes less to joint angle variability than the variability expressed by individuals across repeated movements. This indicates that movement variability is driven more by how the nervous system selects among redundant motor solutions than by body morphology. These findings suggest that clinical assessments of range of motion and movement quality are robust to the method of segment length estimation, supporting the reliability of motion capture-based assessments in both research and clinical settings.