Data from: Adaptive multi-objective control explains how humans make lateral maneuvers while walking

To successfully traverse their environment, humans often perform maneuvers
to achieve desired task goals while simultaneously maintaining balance.
Humans accomplish these tasks primarily by modulating their foot
placements. As humans are more unstable laterally, we must better
understand how humans modulate lateral foot placement. We previously
developed a theoretical framework and corresponding computational models
to describe how humans regulate lateral stepping during straight-ahead
continuous walking. We identified goal functions for step width and
lateral body position that define the walking task and determine the set
of all possible task solutions as Goal Equivalent Manifolds (GEMs). Here,
we used this framework to determine if humans can regulate lateral
stepping during non-steady-state lateral maneuvers by minimizing errors
consistent with these goal functions. Twenty young healthy adults each
performed four lateral lane-change maneuvers in a virtual reality
environment. Extending our general lateral stepping regulation framework,
we first re-examined the requirements of such transient walking tasks.
 Doing so yielded new theoretical predictions regarding how steps
during any such maneuver should be regulated to minimize error costs,
consistent with the goals required at each step and with how these costs
are adapted at each step during the maneuver.  Humans performed
the experimental lateral maneuvers in a manner consistent with our
theoretical predictions. Furthermore, their stepping behavior was well
modeled by allowing the parameters of our previous lateral stepping models
to adapt from step to step. To our knowledge, our results are the first to
demonstrate humans might use evolving cost landscapes in real time to
perform such an adaptive motor task and, furthermore, that such adaptation
can occur quickly – over only one step.  Thus, the predictive
capabilities of our general stepping regulation framework extend to a much
greater range of walking tasks beyond just normal, straight-ahead walking.