DOI: 10.5061/dryad.tx95x6b1x
title: Data from: Adaptive multi-objective control explains how humans make lateral maneuvers while walking

description: 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.
description: Raw kinematic (human motion) data were recorded by a Vicon system
 (https://www.vicon.com/). These data were filtered in Vicon, processed in Matlab to extract the individual stepping data (lateral positions of each foot at each step for each trial, etc) using accepted, previously-published methods, as described in our manuscript. All stepping data are provided in this data set. Stepping data were then further analyzed to assess stepping dynamic for the lateral lane-change task that was the focus of this study and the task that participants executed in our experiments. All codes (Matlab) for these subsequent analyses are provided with this data set.
description: All data and codes provided are written in Matlab (https://www.mathworks.com/) Data files are in Matlab *.mat format There are multiple open-source alternatives to Matlab. Two common alternatives include GNU Octave (https://octave.org/) and SciLab (https://www.scilab.org/), but numerous others exist as well.