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© 2016 by ASME. Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r=0.99 and root mean square error (RMSE)=52.6N medial; average r=0.95 and RMSE=56.6N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE=323 N medial and 348N lateral) and poorly matched contact force shape for the lateral compartment (average r=0.90 medial and-0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal, Postprint (author's final draft)

Neuromusculoskeletal models used to predict muscle and joint contact forces for a specific individual require specification of muscle-tendon, skeletal geometry, and neural control model parameter values. Though these parameter values should ideally be calibrated using in vivo data collected from the subject, they are often taken from generic models. This study explored the influence of three model calibration methods on predicted lower limb muscle and knee contact forces during walking. The calibrated model from each approach was used in a static optimization that predicted knee contact forces for six walking trials. The predictions were evaluated using knee contact forces measured in vivo from a subject implanted with a force-measuring knee replacement. The first calibration approach used muscle-tendon model parameter values (i.e., optimal muscle fiber lengths and tendon slack lengths) taken directly from the literature. The second approach calibrated muscle-tendon model parameter values such that each muscle operated within a physiological range on the ascending region of its normalized force-length curve. The third approach used a novel two-level optimization that exploited knowledge of the knee contact force measurements to calibrate muscle-tendon, moment arm, and neural control model parameter values such that the calibrated model would predict the in vivo contact forces as closely as possible. For the third approach, three walking trials were used to calibrate the model and the remaining three to test the calibrated model. Overall, calibration method had a large affect on predicted knee contact forces. The first method produced highly inaccurate contact force predictions and infeasible solutions for most time frames. The second approach produced accurate medial contact force predictions (average R2 = 0.89, average RMS error = 107 N) but inaccurate lateral predictions (average R2 = -1.77, average RMS error = 297 N). The third approach produced accurate testing, Postprint (published version)