Efficient generation of human-like kinematics in the ballistic phase of point-to-point movement

Human movement is often considered to have two phases: a ballistic phase that brings the limb near the target, and a corrective phase that locates the limb on the target. This article proposes the use of a single non-dimensional curve that can be scaled to produce human-like ballistic movement across a wide range of reaching task configurations. Control of the scaled forcing function requires only that a look-up table of input levels be referenced in response to position measurements. Importantly, the proposed method accurately accounts for the significant kinematic variations evident between individual trials. In comparison, typical feedforward methods replicate the desired mean behavior, but fail to generate the requisite bell-shaped rise and fall in positional variance. Stochastic-optimal feedback control produces the appropriate velocity and variance curves, but carries with it a high computational burden.