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1 U742, INSERM, Paris, France
2 U534, INSERM, Bron, France
3 U371, INSERM, Bron, France
* To whom correspondence should be addressed. E-mail: guigon{at}ccr.jussieu.fr.
The nervous system controls the behavior of complex kinematically redundant biomechanical systems. How it computes appropriate commands to generate movements is unknown. Here we propose a model based on the assumption that the nervous system: 1. processes static (e.g. gravitational) and dynamic (e.g. inertial) forces separately; 2. calculates appropriate dynamic controls to master the dynamic forces and progress toward the goal according to principles of optimal feedback control; 3. uses the size of the dynamic commands (effort) as an optimality criterion; 4. can specify movement duration from a given level of effort. The model was used to control kinematic chains with 2, 4 and 7 degrees of freedom (planar shoulder/elbow, 3D shoulder/elbow, 3D shoulder/elbow/wrist) actuated by pairs of antagonist muscles. The muscles were modeled as second-order nonlinear filters and received the dynamics commands as inputs. Simulations showed that the model can quantitatively reproduce characteristic features of pointing and grasping movements in three-dimensional space, i.e. trajectory, velocity profile, final posture, ... Furthermore, it accounted for amplitude/duration scaling and kinematic invariance for distance and load. These results suggest that motor control could be explained in terms of a limited set of computational principles.
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