Rob den otter & claudine J. c. Lamoth A cautious gait (cG), marked by wider and shorter steps, is typically employed to mitigate expected perturbations proactively. However, it is not well understood if and how cG is informed by the task requirements. therefore, we assessed how cG is adjusted to these requirements. three groups of ten healthy young adults were exposed to a single uninterrupted protocol of treadmill walking that consisted of three distinct phases. Spatiotemporal step characteristics and margins of stability of the unperturbed strides were compared when participants were (i) only warned of a perturbation, (ii) exposed to fifty unilateral (right) slip-like perturbations and (iii) kept unaware of perturbation removal. Only the perturbation intensity predictability differed between groups. This was either kept consistent or pseudo-randomly or randomly varied. Participants walked with wider and shorter steps following the perturbation warning. However, this extinguished in continuing perturbation absence. next, during perturbation exposure, participants shortened the step of the perturbed but increased the step of the unperturbed leg. This did not differ between groups. Finally, participants persisted in displaying CG on perturbation removal, but this extinguished over time. collectively, we show that cG is functionally adjusted to the task requirements. These findings may have practical implications for fall-prevention training. Bipedal gait is a challenging activity, considering that the vertical projection of the centre of mass (CoM) is outside the base of support (BoS) for approximately 80% of the gait cycle 1. In fact, walking involves continuous falling motions, which are caught by a relocation of the BoS during every step. Nevertheless, walking is generally stable and adaptive, as healthy adults are able to adjust their gait continuously to accommodate environmental and task demands 2. However, walking in an environment encountering unpredictable or changing events becomes particularly challenging when the walking performance declines due to natural ageing or pathology 3. As a result, older adults often employ a cautious gait (CG) strategy to safeguard against potential balance loss 4,5. Although this form of anticipatory control has been described as a generic strategy that is characterized by wider and shorter steps 6 , it is not well understood if and how CG is informed by the requirements of the task. The present study addressed how anticipatory control of gait is adjusted to accommodate these requirements. Anticipatory control relies on sensory input and provides the means to identify and proactively accommodate environmental challenges instantaneously 2. Walking on ice is a simple illustration of this, which is typically characterized by wider and shorter steps. These cautious adjustments safeguard dynamic balance and can reduce the need for time-critical, reactive control 7. Putatively, people employ a CG by the necessity of uncertainty (i.e. where, when, and if a perturbation will occur)...

Motor adaptation is a form of learning to re-establish desired movements in novel situations. To probe motor adaptation, one can replicate such conditions experimentally by imposing a sustained perturbation during movement. Exposure to such perturbations initially causes an abrupt change in relevant performance variables, followed by a gradual return to baseline behaviour. The resulting time series exhibit persistent properties related to structural changes in underlying motor control and transitory properties related to trial-to-trial variations. The global trend, signifying the structural change, is often assessed by averaging the time series in predefined bins or nonlinear model fitting. However, these methods to study motor adaptation require a priori decisions to produce accurate fits. Here, we test a data-driven approach called Singular Spectrum Analysis (SSA) to assess the global trend. In SSA, we first decompose the adaptation time series into components that represent either a global trend or additional variations, and secondly, select the component(s) corresponding to the global trend using spectral analysis. In this paper, we will use simulated data to compare the reconstruction performance of SSA with often applied filter and fitting methods in motor adaptation studies and apply SSA to real data obtained during splitbelt adaptation. In the simulations, we show that SSA reconstructed the fast-initial component and entire global trends more accurately than the filtering and fitting methods. In addition, we show that SSA also successfully reconstructed global trends from real data. Therefore, the SSA might be useful in motor learning studies to decompose and assess adaptation time series.

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