Lower Extremity EMG-driven Modeling of Walking

1 Neuromusculoskeletal disorders affecting walking ability are often difficult to manage, in part due to 2 limited understanding of how a patient’s lower extremity muscle excitations contribute to the patient’s 3 lower extremity joint moments. To assist in the study of these disorders, researchers have developed 4 electromyography (EMG) driven neuromusculoskeletal models utilizing scaled generic musculoskeletal 5 geometry. While these models can predict individual muscle contributions to lower extremity joint 6 moments during walking, the accuracy of the predictions can be hindered by errors in the scaled 7 geometry. This study presents a novel EMG-driven modeling method that automatically adjusts 8 surrogate representations of the patient’s musculoskeletal geometry to improve prediction of lower 9 extremity joint moments during walking. In addition to commonly adjusted neuromusculoskeletal model 10 parameters, the proposed method adjusts model parameters defining muscle-tendon lengths, velocities, 11 and moment arms. We evaluated our EMG-driven modeling method using data collected from a high12 functioning hemiparetic subject walking on an instrumented treadmill at speeds ranging from 0.4 to 0.8 13 m/s. EMG-driven model parameter values were calibrated to match inverse dynamic moments for five 14 degrees of freedom in each leg while keeping musculoskeletal geometry close to that of an initial scaled 15 musculoskeletal model. We found that our EMG-driven modeling method incorporating automated 16 adjustment of musculoskeletal geometry predicted net joint moments during walking more accurately 17 than did the same method without geometric adjustments. Geometric adjustments improved moment 18 prediction errors by 25% on average and up to 52%, with the largest improvements occurring at the hip. 19 Predicted adjustments to musculoskeletal geometry were comparable to errors reported in the literature 20 between scaled generic geometric models and measurements made from imaging data. Our results 21 demonstrate that with appropriate experimental data, joint moment predictions for walking generated by 22 an EMG-driven model can be improved significantly when automated adjustment of musculoskeletal 1 geometry is included in the model calibration process. 2