Inverse optimal control of muscle force sharing during pathological gait

Muscle force sharing is typically resolved by minimizing a specific objective function to approximate neural control strategies. An inverse optimal control approach was applied to identify the "best" objective function, among a positive linear combination of basis objective functions, associated with the gait of two post-stroke males, one high-functioning and one low-functioning. By comparing objective-function-predicted muscle forces to those of a reference EMG-driven model, using Root-Mean-Squared-Errors (RMSE) and Pearson Correlation Coefficients (CC), it was found that the "best" objective function is subject- and leg-specific. No single function works universally well, yet the best options are usually differently weighted combinations of muscle activation- and power-minimization. Subject-specific inverse optimal control models performed best on their respective limbs (RMSE 178/213 N, CC 0.71/0.61 for respective legs of subject 1; RMSE 205/165 N, CC 0.88/0.85 for respective legs of subject 2), but cross-subject generalization was poor, particularly for paretic legs. Moreover, minimizing the root mean square of muscle power emerged as important for paretic limbs, while minimizing activation-based functions dominated for non-paretic limbs. This may suggest different neural control strategies between affected and unaffected sides, possibly altered by the presence of spasticity. Among the 15 considered objective functions commonly used in inverse dynamics-based computations, the root mean square of muscle power was the only one explicitly incorporating muscle velocity, leading to a possible model for spasticity in the paretic limbs. Although this objective function has been rarely used, it may be relevant for modeling pathological gait, such as post-stroke gait.