A Computational Model of Muscle Recruitment for Wrist Movements

Abstract

To execute a movement, the CNS must appropriately select and activate the set of muscles that will produce the desired movement. This problem is particularly difficult because a variety of muscle subsets can usually be used to produce the same joint motion. The motor system is therefore faced with a motor redundancy problem that must be resolved to produce the movement. In this paper, we present a model of muscle recruitment in the wrist step-tracking task. Muscle activation levels for five muscles are selected so as to satisfy task constraints (moving to the designated target) while also minimizing a measure of the total effort in producing the movement. Imposing these constraints yields muscle activation patterns qualitatively similar to those observed experimentally. In particular, the model reproduces the observed cosine-like recruitment of muscles as a function of movement direction and also appropriately predicts that certain muscles will be recruited most strongly in movement directions that differ significantly from their direction of action. These results suggest that the observed recruitment behavior may not be an explicit strategy employed by the nervous system, but instead may result from a process of movement optimization.