A bang-bang control model predicts the triphasic muscles activity during hand reaching

There are numerous ways to reach for an apple hanging from a tree. Yet, our motor system uses a specific muscle activity pattern that features activity bursts and silent periods. We suggest that these bursts are an evidence against the common view that the brain controls the commands to the muscles in a smooth continuous manner. Instead, we propose a model in which a motor plan is transformed into a piecewise-constant control signal that is low-pass filtered and transmitted to the muscles with different muscle-specific delays. We use a Markov chain Monte Carlo (MCMC) method to identify transitions in the state of the muscles following initial activation and show that fitting a bang-bang control model to the kinematics of movement predicts these transitions in the state of the muscles. Such a bang-bang controller suggests that the brain reduces the complexity of the problem of ballistic movements control by sending commands to the muscles at sparse times. Identifying this bang-bang controller can be useful to develop efficient controllers for neuroprostheses and other physical human-robot interaction systems.NEW & NOTEWORTHY While ballistic hand reaching movements are characterized by smooth position and velocity signals, the activity of the muscles exhibits bursts and silent periods. Here, we propose that a model based on bang-bang control provides the link between the abrupt changes in the muscle activity and the smooth reaching trajectory. Using bang-bang control instead of continuous control may simplify the design of prostheses and other physical human-robot interaction systems.