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Although we can measure muscle activity and analyze their activationpatterns, we understand little about how individual muscles affect the jointtorque generated. It is known that they are controlled by circuits in thespinal cord, a system much less well understood than the cortex. Knowing thecontribution of the muscles towards a joint torque would improve ourunderstanding of human limb control. We present a novel framework to examinethe control of biomechanics using physics simulations informed byelectromyography (EMG) data. These signals drive a virtual musculoskeletalmodel in the Neurorobotics Platform (NRP), which we then use to evaluateresulting joint torques. We use our framework to analyze raw EMG data collectedduring an isometric knee extension study to identify synergies that drive amusculoskeletal lower limb model. The resulting knee torques are used as areference for genetic algorithms (GA) to generate new simulated activationpatterns. On the platform the GA finds solutions that generate torques matchingthose observed. Possible solutions include synergies that are similar to thoseextracted from the human study. In addition, the GA finds activation patternsthat are different from the the biological ones while still producing the sameknee torque. The NRP forms a highly modular integrated simulation platformallowing these in silico experiments. We argue that our framework allows forresearch of the neurobiomechanical control of muscles during tasks, which wouldotherwise not be possible.

Evaluating Muscle Synergies with EMG Data and Physics Simulation in the Neurorobotics Platform