Most often, gait biomechanics is studied during straight-ahead walking. However, real-life walking imposes various lateral maneuvers people must navigate. Such maneuvers challenge people's lateral balance and can induce falls. Determining how people regulate their stepping movements during such complex walking tasks is therefore essential. Here, 24 adults (12F/12M; Age 25.8±3.5yrs) walked on wide or narrow virtual paths that were either straight, slowly-winding, or quickly-winding. From each trial, we computed time series of participants' step widths and their lateral body positions relative to their path. We applied our Goal Equivalent Manifold framework - an analysis of how task-level redundancy impacts motor regulation - to quantify how participants adjusted their step width and lateral position from step to step as they walked on these paths. On the narrower paths, participants walked with narrower steps and less lateral position and step width variability. They did so by correcting step-to-step deviations in lateral position more, while correcting step-to-step deviations in step width less. On the winding paths, participants took both narrower and more variable steps. Interestingly, on slowly-winding paths, participants corrected step-to-step deviations in step width more by correcting step-to-step deviations in lateral position less: i.e., they prioritized maintaining step width over position. Conversely, on quickly-winding paths, participants strongly corrected step-to-step deviations in both step width and lateral position: i.e., they prioritized maintaining both approximately equally, consistent with trying to maximize their maneuverability. These findings have important implications for persons who have elevated fall risk.

People with balance impairments often struggle performing turns or lateral maneuvers, which can increase risk of falls and injuries. Here we asked how people’s mediolateral balance is impacted when walking on non-straight winding paths. Twenty-four healthy adults (12F / 12M; 25.8±3.5 yrs) participated. Each walked on each of six paths projected onto a treadmill, comprised of three pseudo-random path oscillation frequency combinations (straight, slowly-winding, quickly-winding), each presented at either wide or narrow width. We quantified stepping errors as the percent of steps taken off each path. We quantified minimum mediolateral Margin of Stability (MoSL) at each step and calculated means (μ) and standard deviations (σ) for each trial. We calculated lateral Probability of Instability (PoIL) as participants’ statistical risk of taking unstable (MoSL < 0) steps. On narrower paths, participants made more stepping errors and walked with smaller μ(MoSL) for all path frequencies (p < 0.001), and exhibited increased PoIL on the straight and slowly-winding paths (p < 0.001). On winding paths, participants made progressively more stepping errors and walked with smaller μ(MoSL) as oscillation frequency increased on narrow paths (all p < 0.001) and on the wide quickly-winding paths (all p < 0.001). They also consistently walked with larger σ(MoSL), and increased PoILon higher sinuosity paths of both widths (all p < 0.001). Though many took numerous unstable steps, no participant fell. Our results demonstrate healthy adults’ ability both to trade off increased risk of lateral instability for greater maneuverability, and to employ highly-versatile stepping strategies to maintain balance while walking.

People rarely walk in straight lines. Instead, we make frequent turns or other maneuvers. Spatiotemporal parameters fundamentally characterize gait. For straight walking, these parameters are well-defined for the task of walking on a straight path. Generalizing these concepts to non-straight walking, however, is not straightforward. People follow non-straight paths imposed by their environment (sidewalk, windy hiking trail, etc.) or choose readily-predictable, stereotypical paths of their own. People actively maintain lateral position to stay on their path and readily adapt their stepping when their path changes. We therefore propose a conceptually coherent convention that defines step lengths and widths relative to predefined walking paths. Our convention simply re-aligns lab-based coordinates to be tangent to a walker's path at the mid-point between the two footsteps that define each step. We hypothesized this would yield results both more correct and more consistent with notions from straight walking. We defined several common non-straight walking tasks: single turns, lateral lane changes, walking on circular paths, and walking on arbitrary curvilinear paths. For each, we simulated idealized step sequences denoting "perfect" performance with known constant step lengths and widths. We compared results to path-independent alternatives. For each, we directly quantified accuracy relative to known true values. Results strongly confirmed our hypothesis. Our convention returned vastly smaller errors and introduced no artificial stepping asymmetries across all tasks. All results for our convention rationally generalized concepts from straight walking. Taking walking paths explicitly into account as important task goals themselves thus resolves conceptual ambiguities of prior approaches.

People walk in complex environments where they must adapt their steps to maintain balance and satisfy changing task goals. How people do this is not well understood. We recently developed computational models of lateral stepping, based on Goal Equivalent Manifolds that serve as motor regulation templates, to identify how people regulate walking movements from step-to-step. In normal walking, healthy adults strongly maintain step width, but also lateral position on their path. Here, we used this framework to pose empirically-testable hypotheses about how humans might adapt their lateral stepping dynamics when asked to prioritize different stepping goals. Participants walked on a treadmill in a virtual-reality environment under 4 conditions: normal walking and, while given direct feedback at each step, walking while trying to maintain constant step width, constant absolute lateral position, or constant heading (direction). Time series of lateral stepping variables were extracted, and variability and statistical persistence (reflecting step-to-step regulation) quantified. Participants exhibited less variability of the prescribed stepping variable compared to normal walking during each feedback condition. Stepping regulation results supported our models' predictions: to maintain constant step width or position, people either maintained or increased regulation of the prescribed variable, but also decreased regulation of its complement. Thus, people regulated lateral foot placements in predictable and systematic ways determined by specific task goals. Humans regulate stepping movements to not only "just walk" (step without falling), but also to achieve specific goal-directed tasks within a specific environment. The framework and motor regulation templates presented here capture these important interactions.