Abstract
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 musculo-tendon network, and a neuromuscular network. We model the relationship between the spinal nerve and muscle tension by a neural network. The resulting parameters match well with the agonist-antagonist relationship of the 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 the 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.
