Challenging human locomotion: stability and modular organisation in unsteady conditions

Abstract

The need to move over uneven terrain is a daily challenge. In order to face unexpected perturbations due to changes in the morphology of the terrain, the central nervous system must flexibly modify its control strategies. We analysed the local dynamic stability and the modular organisation of muscle activation (muscle synergies) during walking and running on an even- and an uneven-surface treadmill. We hypothesized a reduced stability during uneven-surface locomotion and a reorganisation of the modular control. We found a decreased stability when switching from even- to uneven-surface locomotion (p < 0.001 in walking, p = 0.001 in running). Moreover, we observed a substantial modification of the time-dependent muscle activation patterns (motor primitives) despite a general conservation of the time-independent coefficients (motor modules). The motor primitives were considerably wider in the uneven-surface condition. Specifically, the widening was significant in both the early (+40.5%, p < 0.001) and late swing (+7.7%, p = 0.040) phase in walking and in the weight acceptance (+13.6%, p = 0.006) and propulsion (+6.0%, p = 0.041) phase in running. This widening highlighted an increased motor output’s robustness (i.e. ability to cope with errors) when dealing with the unexpected perturbations. Our results confirmed the hypothesis that humans adjust their motor control strategies’ timing to deal with unsteady locomotion.

Modular organization of murine locomotor pattern in the presence and absence of sensory feedback from muscle spindles

“…In walking, we assigned 100 of the 200 total points to the stance and the remaining 100 points to the swing phase (Santuz et al . ). The reason for this choice is twofold (Santuz et al .…”

“…A fundamental synergy can be defined as an activation pattern whose motor primitive shows a single main peak of activation (Santuz et al . ). When two or more fundamental synergies are blended into one, a combined synergy appears.…”

“…using the classical Gaussian NMF algorithm (Lee & Seung, ; Santuz et al . , , b ). The code is available at Zenodo (https://doi.org/10.5281/zenodo.2588332), together with some example data.…”

“…We compared motor primitives by evaluating the coefficient of determination ( R 2 , a metric we also employed, as described above, to estimate the similarity between the original and the reconstructed EMG data after factorization; Santuz et al . ), the centre of activity (CoA) and the full width at half‐maximum (FWHM). We previously found in humans that typical R 2 values between intraday measurements are, on average, bigger than 0.74 for motor primitives (Santuz et al .…”

Methods for Measuring Swallowing Pressure Variability Using High-Resolution Manometry

“…We can modulate the degree of variability used during motor learning, as either part of a reward [45] or error-based learning process [46], and this modulation in variability eventually decreases once we master a new motor skill [47][48][49]. Movement variability is also critical for responding to permutations in the environment and producing on-line corrections [44,50,51]. On the other hand, pathologically increased movement variability has been characterized in many neurologic conditions, including Parkinson disease [52][53][54][55][56], stroke [39,57], dystonia [41,58], developmental coordination disorder [59,60], and cerebral palsy [61,62], to name a few.…”