The influence of the way the muscle force is modeled on the predicted results obtained by solving indeterminate problems for a fast elbow flexion.
A critical point in models of the human limbs when the aim is to investigate the motor control is the muscle model. More often the mechanical output of a muscle is considered as one musculotendon force that is a design variable in optimization tasks solved predominantly by static optimization. For dynamic conditions, the relationship between the developed force, the length and the contraction velocity of a muscle becomes important and rheological muscle models can be incorporated in the optimization tasks. Here the muscle activation can be a design variable as well. Recently a new muscle model was proposed. A muscle is considered as a mixture of motor units (MUs) with different peculiarities and the muscle force is calculated as a sum of the MUs twitches. The aim of the paper is to compare these three ways for presenting the muscle force. Fast elbow flexion is investigated using a planar model with five muscles. It is concluded that the rheological models are suitable for calculation of the current maximal muscle forces that can be used as weight factors in the objective functions. The model based on MUs has many advantages for precise investigations of motor control. Such muscle presentation can explain the muscle co-contraction and the role of the fast and the slow MUs. The relationship between the MUs activation and the mechanical output is more clear and closer to the reality.
Raikova
Rositsa
R
Centre of Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria. rosi.raikova@clbme.bas.bg
Aladjov
Hristo
H
D003198:Computer Simulation; D004551:Elbow Joint / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D018485:Muscle Fibers, Skeletal / Q000502:physiology*; D004856:Postural Balance / Q000502:physiology*; D012212:Rheology / Q000379:methods*; D013647:Task Performance and Analysis