Humans optimally anticipate and compensate for an uneven step during walking

Abstract

The simple task of walking up a sidewalk curb is actually a dynamic prediction task. The curb is a disturbance that causes a loss of momentum, to be anticipated and compensated for. For example, the compensation might regain momentum and ensure undisturbed time of arrival. But without a selection criterion, there are infinite possible strategies. Here we show that humans compensate with an anticipatory pattern of forward speed adjustments, with a criterion of minimizing mechanical energy input. This is predicted by optimal control for a simple model of walking dynamics, with each legs push-off work as input. Optimization predicts a tri-phasic trajectory of speed (and thus momentum) adjustments, including an anticipatory, feedforward phase. In experiment, human subjects successfully regain time relative to undisturbed walking, with the predicted tri-phasic trajectory. They also scale the pattern with up- or down-steps, and inversely with average speed, as also predicted by model. Humans can reason about the dynamics of walking to plan anticipatory and economical control, even with a sidewalk curb in the way.