  Although we can measure muscle activity and analyze their activation
patterns, we understand little about how individual muscles affect the joint
torque generated. It is known that they are controlled by circuits in the
spinal cord, a system much less well understood than the cortex. Knowing the
contribution of the muscles towards a joint torque would improve our
understanding of human limb control. We present a novel framework to examine
the control of biomechanics using physics simulations informed by
electromyography (EMG) data. These signals drive a virtual musculoskeletal
model in the Neurorobotics Platform (NRP), which we then use to evaluate
resulting joint torques. We use our framework to analyze raw EMG data collected
during an isometric knee extension study to identify synergies that drive a
musculoskeletal lower limb model. The resulting knee torques are used as a
reference for genetic algorithms (GA) to generate new simulated activation
patterns. On the platform the GA finds solutions that generate torques matching
those observed. Possible solutions include synergies that are similar to those
extracted from the human study. In addition, the GA finds activation patterns
that are different from the the biological ones while still producing the same
knee torque. The NRP forms a highly modular integrated simulation platform
allowing these in silico experiments. We argue that our framework allows for
research of the neurobiomechanical control of muscles during tasks, which would
otherwise not be possible.
