An approximate stochastic optimal control framework to simulate nonlinear neuro-musculoskeletal models in the presence of noise.

ABSTRACT: Optimal control simulations have shown that both musculoskeletal dynamics and physiological noise are important determinants of movement. However, due to the limited efficiency of available computational tools, deterministic simulations of movement focus on accurately modelling the musculoskeletal system while neglecting physiological noise, and stochastic simulations account for noise while simplifying the dynamics. We took advantage of recent approaches where stochastic optimal control problems are approximated using deterministic optimal control problems, which can be solved efficiently using direct collocation. We were thus able to extend predictions of stochastic optimal control as a theory of motor coordination to include muscle coordination and movement patterns emerging from non-li

SUBMITTER: Van Wouwe T 

PROVIDER: S-EPMC9176817 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature