Evaluating cost function criteria in predicting healthy gait

Accurate predictive simulations of human gait rely on optimisation criteria to solve the system’s redundancy. Defining such is challenging, as objectives driving optimization are unclear. This study evaluated how minimising various physiologically-based (i.e., cost transport, muscle activity, head stability, foot–ground impact, and knee ligament use) affects predicted gait, developed a combined, weighted function tuned predict healthy gait. A generic planar musculoskeletal model with 18 Hill-type muscles was actuated using reflex-based, parameterized controller. First, were applied into base simulation framework separately. The pattern by each criterion compared experimental data coefficients determination (R2) root mean square errors (RMSE) averaged over all biomechanical variables. Second, optimal combined created through stepwise addition criteria. Third, performance resulting comparing that optimised solely track data. Optimising for separately showed their individual contribution distinct aspects (overall R2: 0.37–0.56; RMSE: 3.47–4.63 SD). An optimally provided improved overall agreement 0.72; 2.10 SD), its close what maximally achievable underlying framework. contribute synthesising careful weighting them essential in predicting