Prediction of Muscle Activations for Reaching Movements using Deep Neural Networks

The motor control problem involves determining the time-varying muscle activation trajectories required to accomplish a given movement. Muscle redundancy makes motor control a challenging task: there are many possible activation trajectories that accomplish the same movement. Despite this redundancy, most movements are accomplished in highly stereotypical ways. For example, point-topoint reaching movements are almost universally performed with very similar smooth trajectories [1].