the utility of an empirically derived co-activation ratio for muscle force prediction through optimization

biomechanical optimization models that apply efficiency-based objective functions often underestimate or negate antagonist co-activation. co-activation assists movement control, joint stabilization and limb stiffness and should be carefully incorporated into models. the purposes of this study were to mathematically describe co-activation relationships between elbow flexors and extensors during isometric exertions at varying intensity levels and postures, and secondly, to apply these co-activation relationships as constraints in an optimization muscle force prediction model of the elbow and assess changes in predictions made while including these constraints. sixteen individuals performed 72 isometric exertions while holding a load in their right hand. surface emg was recorded from elbow flexors and extensors. a co-activation index provided a relative measure of flexor contribution to total activation about the elbow. parsimonious models of co-activation during flexion and extension exertions were developed and added as constraints to a muscle force prediction model to enforce co-activation. three different pcsa data sets were used. elbow co-activation was sensitive to changes in posture and load. during flexion exertions the elbow flexors were activated about 75% mvc (this amount varied according to elbow angle, shoulder flexion and abduction angles, and load). during extension exertions the elbow flexors were activated about 11% mvc (this amount varied according to elbow angle, shoulder flexion angle and load). the larger pcsa values appeared to be more representative of the subject pool. inclusion of these co-activation constraints improved the model predictions, bringing them closer to the empirically measured activation levels.