Macroscopic modeling and identification of the human neuromuscular network.

In this paper, we build a mathematical model of the whole-body neuromuscular network and identify its parameters by optical motion capture, inverse dynamics computation, and statistical analysis. The model includes a skeleton, a musculotendon network, and a neuromuscular network. The skeleton is composed of 155 joints representing the inertial property and mobility of the human body. The musculotendon network includes more than 1000 muscles, tendons, and ligaments modeled as ideal wires with any number of via points. We also develop an inverse dynamics algorithm to estimate the muscle tensions required to perform a given motion sequence. Finally, we model the relationship between the spinal nerve signals and muscle tensions by a neural network. The resulting parameters match well with the agonist-antagonist relationships of muscles. We also demonstrate that we can simulate the patellar tendon reflex using the neuromuscular model. This is the first attempt to build and identify a macroscopic model of the human neuromuscular network based only on non-invasive motion measurements, and the result implies that the activation commands from the motor neurons can be considerably simple compared with the number of muscles to be controlled.