Abstract
Musculoskeletal modeling techniques simulate reverse total shoulder arthroplasty (RTSA) shoulders and how implant placement affects muscle moment arms. Yet, studies have not taken into account how muscle-length changes affect force-generating capacity postoperatively. We develop a patient-specific model for RTSA patients to predict muscle activation.
Patient-specific muscle parameters were estimated using an optimization scheme calibrating the model to isometric arm abduction data at 0°, 45°, and 90°. We compared predicted muscle activation to experimental electromyography recordings. A twelve-degree of freedom model with experimental measurements created patient-specific data estimating muscle parameters corresponding to strength. Optimization minimized the difference between measured and estimated joint moments and muscle activations, yielding parameters corresponding to subjects' strength that can predict muscle activation and lengths.
Model calibration was performed on RTSA patients' arm abduction data. Predicted muscle activation ranged between 3% and 70% of maximum. The maximum joint moment produced was 10 Nm. The model replicated measured moments accurately (R2 > 0.99). The optimized muscle parameters produced feasible muscle moments and activations for dynamic arm abduction when using data from isometric force trials. A normalized correlation was found between predicted and experimental muscle activation for dynamic abduction (r > 0.9); the moment generation to lift the arm was tracked (R2 = 0.99).
Statement of Clinical Significance: We developed a framework to predict patient-specific muscle parameters. Combined with patient-specific models incorporating joint configurations, kinematics, and bone anatomy, they can predict muscle activation in novel tasks and, e.g., predict how RTSA implant and surgical decisions may affect muscle function.
