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 (R. 2. > 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 (R. 2. = 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

Modern musculoskeletal modeling techniques have been used to simulate shoulders with reverse total shoulder arthroplasty and study how geometric changes resulting from implant placement affect shoulder muscle moment arms. These studies do not, however, take into account how changes in muscle length will affect the force generating capacity of muscles in their post-operative state. The goal of this study was to develop and calibrate a patient-specific shoulder model for subjects with RTSA in order to predict muscle activation during dynamic activities. Patient-specific muscle parameters were estimated using a nested optimization scheme calibrating the model to isometric arm abduction data at 0°, 45° and 90°. The model was validated by comparing predicted muscle activation for dynamic abduction to experimental electromyography recordings. A twelve-degree of freedom model was used with experimental measurements to create a set of patient-specific data (three-dimensional kinematics, muscle activations, muscle moment arms, joint moments, muscle lengths, muscle velocities, tendon slack lengths, optimal fiber lengths and peak isometric forces) estimating muscle parameters corresponding to each patient's measured strength. The optimization varied muscle parameters to minimize the difference between measured and estimated joint moments and muscle activations for isometric abduction trials. This optimization yields a set of patient-specific muscle parameters corresponding to the subject's own muscle strength that can be used to predict muscle activation and muscle lengths for a range of dynamic activities. The model calibration/optimization procedure was performed on arm abduction data for a subject with reverse total shoulder arthroplasty. Muscle activation predicted by the model ranged between 3% and 90% of maximum. The maximum joint moment produced was 20 Nm. The model replicated measured joint moments accurately (R2 > 0.99). The optimized muscle parameter set produced feasible muscle moments and muscle activations for dynamic arm abduction, when calibrated using data from isometric force trials. Current modeling techniques for the upper extremity focus primarily on geometric changes and their effects on shoulder muscle moment arms. In an effort to create patient-specific models, we have developed a framework to predict subject-specific muscle parameters. These estimated muscle parameters, in combination with patient-specific models that incorporate the patient's joint configurations, kinematics and bone anatomy, provide a framework to predict dynamic muscle activation in novel tasks and, for example, predict how joint center changes with reverse total shoulder arthroplasty may affect muscle function