Abstract

The nervous system activates a pair of agonist and antagonist muscles to determine the muscle activation pattern for a desired movement. Although there is a problem with redundancy, it is solved immediately, and movements are generated with characteristic muscle activation patterns in which antagonistic muscle pairs show alternate bursts with a triphasic shape. To investigate the requirements for deriving this pattern, this study simulated arm movement numerically by adopting a musculoskeletal arm model and an optimal control. The simulation reproduced the triphasic electromyogram (EMG) pattern observed in a reaching movement using a cost function that considered three terms: end-point position, velocity, and force required; the function minimised neural input. The first, second, and third bursts of muscle activity were generated by the cost terms of position, velocity, and force, respectively. Thus, we concluded that the costs of position, velocity, and force requirements in optimal control can induce triphasic EMG patterns. Therefore, we suggest that the nervous system may control the body by using an optimal control mechanism that adopts the costs of position, velocity, and force required; these costs serve to initiate, decelerate, and stabilise movement, respectively.

Highlights

  • The nervous system activates a pair of agonist and antagonist muscles to determine the muscle activation pattern for a desired movement

  • We re-examined the triphasic muscle activation patterns observed in EMGs during arm movement from the perspective of optimal control using an optimal feedback control (OFC)-like cost function that consisted of terminal requirement costs with minimised neural input to predict the muscle activation patterns

  • We performed numerical simulations involving application of an iterative linear–quadratic–Gaussian (ILQG) m­ ethod[35], which approximates OFC, to physiological arm dynamics with a realistic muscle model for macaque monkeys. This revealed that the optimal control could selectively tune muscles according to movement direction, and it indicated an interlaced cost based on combinations of the terminal requirement of the end-point position, velocity, and force under minimisation of neural input during movement

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Summary

Introduction

The nervous system activates a pair of agonist and antagonist muscles to determine the muscle activation pattern for a desired movement. We performed numerical simulations involving application of an iterative linear–quadratic–Gaussian (ILQG) m­ ethod[35], which approximates OFC, to physiological arm dynamics with a realistic muscle model for macaque monkeys This revealed that the optimal control could selectively tune muscles according to movement direction, and it indicated an interlaced cost based on combinations of the terminal requirement of the end-point position, velocity, and force under minimisation of neural input during movement. We suggest that the neural system controls the body by using an optimal control mechanism based on a cost function that consists of position, velocity, and force requirements These requirements correspond with the first (AG1), second (ANT), and third (AG2) muscle activation bursts, which serve to initiate, decelerate, and stabilise movement, respectively

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