Modeling and identifying the somatic reflex network of the human neuromuscular system

In this paper, we build a mathematical model of the whole-body neuromuscular network and identify its parameters by optical motion capture, inverse kinematics, 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 somatic reflex network based on the relationship between the spinal nerves and the muscle tensions by a neural network. The resulting parameters match well with the agonist-antagonist relationship of the muscles. We also demonstrate that the model inherently includes low-level somatic reflexes such as the patellar tendon reflex using the neuromuscular model. This is the attempt to build and identify the neuromuscular network based only on non-invasive motion measurements, and the result shows that the whole-body muscles can be controlled by the command signals as few as the number of spinal nerve rami.