Exploring muscle recruitment by Bayesian methods during motion

Abstract

The human musculoskeletal system is characterized by redundancy in the sense that the number of muscles exceeds the number of degrees of freedom of the musculoskeletal system. In practice, this means that a given motor task can be performed by activating the muscles in infinitely many different ways. This redundancy is important for the functionality of the system under changing external or internal conditions, including different diseased states. A central problem in biomechanics is how, and based on which principles, the complex of central nervous system and musculoskeletal system selects the normal activation patterns, and how the patterns change under various abnormal conditions including neurodegenerative diseases and aging. This work lays the mathematical foundation for a formalism to address the question, based on Bayesian probabilistic modeling of the musculoskeletal system. Lagrangian dynamics is used to translate observations of the movement of a subject performing a task into a time series of equilibria which constitute the likelihood model. Different prior models corresponding to biologically motivated assumptions about the muscle dynamics and control are introduced. The posterior distributions of muscle activations are derived and explored by using Markov chain Monte Carlo (MCMC) sampling techniques. The different priors can be analyzed by comparing the model predictions with actual observations.