Birgitt Peeters

Conventional musculoskeletal spine models often rely on deterministic kinematic constraints to estimate spinopelvic kinematics and kinetics, which can produce unrealistic intervertebral motions and loading patterns. Incorporating intervertebral stiffness in kinematic evaluations would improve physiological accuracy, particularly during lifting tasks where spinal loading is critical. We developed a stiffness-dependent kinematics estimation workflow that integrates nonlinear intervertebral stiffness within an optimal control formulation, enabling spinal motion to emerge from mechanical behaviour rather than prescribed kinematic constraints in full body musculoskeletal models. Eleven healthy participants performed trunk flexion, lateral bending, and axial rotation tasks, with and without lifting a load. Predicted kinematics and compressive forces were compared against a conventional constraints-driven approach. A single-subject dataset with biplanar radiography provided ground-truth validation of vertebral motions. The stiffness-dependent method generated smoother, more physiologically plausible motion distributions and consistently reduced lumbar compressive loading. Vertebral orientation and position errors were lower than with the constraints-driven approach, and compressive forces aligned with literature ranges. Nonlinear stiffness modelling yields more evenly distributed spinal kinematics and reduced lumbar loading, providing a physiologically grounded framework for spine biomechanics and injury prevention research.

Background: Spine kinematics assessment is crucial for understanding intervertebral joint motion, particularly in conditions like spinal deformity, which alters and reduces spinal motion. Estimating spine kinematics in vivo usually relies on kinematic constraints to reduce the degrees of freedom in musculoskeletal models, but they lack standardization and fail to generalize across populations. This study proposes a novel method utilizing coordinate optimization instead of kinematic constraints, aiming to improve the generalizability and accuracy of spine kinematics estimation across different populations and marker protocols. Methods: This study used two retrospective datasets: 13 subjects with spinal deformities and 11 healthy individuals. Spine kinematics were estimated by minimizing errors between simulated and experimental marker positions and penalizing large intervertebral joint angles. 3D orientation and position errors against image‐based ground truth vertebral orientations and positions and experimental marker positions were calculated and compared for eight different weight settings. The accuracy was further assessed using standard error of measurements (SEM) compared to kinematic constraint methods. Results: The best‐performing optimization settings resulted in average vertebral orientation errors of 5.1°, 3.2°, and 3.2° for axial rotation, lateral bending, and flexion‐extension, respectively, and 3D position errors of 7.7 mm. These values reflect the average of vertebra‐specific errors within each subject, further averaged across all subjects in the deformity dataset. Similarly, in the healthy dataset, average 3D marker errors remained below 1 cm, and SEM values remained below 1.3°. Conclusions: The coordinate optimization method showed robust performance, achieving high accuracy in vertebral orientation and position (deformity) and marker tracking (healthy). This method consistently matched or surpassed state‐of‐the‐art kinematic constraints methods while introducing generalizability across different populations and marker protocols.