Using Bayesian inference to estimate plausible muscle forces in musculoskeletal models.

ABSTRACT: Musculoskeletal modeling is currently a preferred method for estimating the muscle forces that underlie observed movements. However, these estimates are sensitive to a variety of assumptions and uncertainties, which creates difficulty when trying to interpret the muscle forces from musculoskeletal simulations. Here, we describe an approach that uses Bayesian inference to identify plausible ranges of muscle forces for a simple motion while representing uncertainty in the measurement of the motion and the objective function used to solve the muscle redundancy problem. We generated a reference elbow flexion-extension motion and computed a set of reference forces that would produce the motion while minimizing muscle excitations cubed via OpenSim Moco. We then used a Markov Chain Monte Carlo (M

SUBMITTER: Johnson RT 

PROVIDER: S-EPMC8944069 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature