Journal Name: Current Bioinformatics
Article Title: Signal-Dependent Noise Induces Muscle Co-Contraction to Achieve Required Movement Accuracy: A Simulation Study with an Optimal Control
Author(s): Yuki Ueyama and Eizo Miyashita
Abstract
Simultaneous activation of the agonist and antagonist muscles surrounding a joint, called co-contraction, is suggested to play a role in increasing joint stiffness to improve movement accuracy. However, it has not been clarified how co-contraction is related to movement accuracy, as most models for motor planning and control cannot deal with muscle co-contraction. In this study, the muscle activation and joint stiffness in reaching movements were studied under three different requirement levels of endpoint accuracy using a two-joint six-muscle model and an approximately optimal control. We carried out simulations of biological arm movements for a center-out reaching task under different accuracy demands with different types of motor noise and demonstrated time-varying co-contraction and a double-peaked jointstiffness profile. Furthermore, we showed that the strength of co-contraction and joint stiffness increased depending on the required accuracy level under signal-dependent noise, the magnitude of which was proportional to the motor command but not to additive Gaussian noise. We concluded that the optimal control is a valid model for the human motor control system and that signal-dependent noise is essential to induce co-contraction depending on accuracy demands.
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