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      Muscle Activation Patterns Are More Constrained and Regular in Treadmill Than in Overground Human Locomotion

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          Abstract

          The use of motorized treadmills as convenient tools for the study of locomotion has been in vogue for many decades. However, despite the widespread presence of these devices in many scientific and clinical environments, a full consensus on their validity to faithfully substitute free overground locomotion is still missing. Specifically, little information is available on whether and how the neural control of movement is affected when humans walk and run on a treadmill as compared to overground. Here, we made use of linear and non-linear analysis tools to extract information from electromyographic recordings during walking and running overground, and on an instrumented treadmill. We extracted synergistic activation patterns from the muscles of the lower limb via non-negative matrix factorization. We then investigated how the motor modules (or time-invariant muscle weightings) were used in the two locomotion environments. Subsequently, we examined the timing of motor primitives (or time-dependent coefficients of muscle synergies) by calculating their duration, the time of main activation, and their Hurst exponent, a non-linear metric derived from fractal analysis. We found that motor modules were not influenced by the locomotion environment, while motor primitives were overall more regular in treadmill than in overground locomotion, with the main activity of the primitive for propulsion shifted earlier in time. Our results suggest that the spatial and sensory constraints imposed by the treadmill environment might have forced the central nervous system to adopt a different neural control strategy than that used for free overground locomotion, a data-driven indication that treadmills could induce perturbations to the neural control of locomotion.

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          Most cited references72

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          Learning the parts of objects by non-negative matrix factorization.

          Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.
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            Optimal feedback control and the neural basis of volitional motor control.

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              Optimal feedback control as a theory of motor coordination.

              A central problem in motor control is understanding how the many biomechanical degrees of freedom are coordinated to achieve a common goal. An especially puzzling aspect of coordination is that behavioral goals are achieved reliably and repeatedly with movements rarely reproducible in their detail. Existing theoretical frameworks emphasize either goal achievement or the richness of motor variability, but fail to reconcile the two. Here we propose an alternative theory based on stochastic optimal feedback control. We show that the optimal strategy in the face of uncertainty is to allow variability in redundant (task-irrelevant) dimensions. This strategy does not enforce a desired trajectory, but uses feedback more intelligently, correcting only those deviations that interfere with task goals. From this framework, task-constrained variability, goal-directed corrections, motor synergies, controlled parameters, simplifying rules and discrete coordination modes emerge naturally. We present experimental results from a range of motor tasks to support this theory.
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                Author and article information

                Contributors
                Journal
                Front Bioeng Biotechnol
                Front Bioeng Biotechnol
                Front. Bioeng. Biotechnol.
                Frontiers in Bioengineering and Biotechnology
                Frontiers Media S.A.
                2296-4185
                23 October 2020
                2020
                : 8
                : 581619
                Affiliations
                [1] 1Department of Mechanical and Aerospace Engineering, Sapienza University of Rome , Rome, Italy
                [2] 2Department of Electrical Engineering and Informatics, Technische Universität Berlin , Berlin, Germany
                [3] 3Department of Training and Movement Sciences, Humboldt-Universität zu Berlin , Berlin, Germany
                [4] 4Berlin School of Movement Science, Humboldt-Universität zu Berlin , Berlin, Germany
                Author notes

                Edited by: Giacomo Severini, University College Dublin, Ireland

                Reviewed by: Michael James MacLellan, University of Prince Edward Island, Canada; Anitha Manohar, Merck, United States

                *Correspondence: Alessandro Santuz alessandro.santuz@ 123456hu-berlin.de

                This article was submitted to Bionics and Biomimetics, a section of the journal Frontiers in Bioengineering and Biotechnology

                †These authors have contributed equally to this work and are listed in alphabetical order

                Article
                10.3389/fbioe.2020.581619
                7644811
                33195143
                20ccc0ac-541a-49a7-b07f-e589fd2f8fa8
                Copyright © 2020 Mileti, Serra, Wolf, Munoz-Martel, Ekizos, Palermo, Arampatzis and Santuz.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 July 2020
                : 15 September 2020
                Page count
                Figures: 7, Tables: 1, Equations: 2, References: 72, Pages: 16, Words: 10720
                Categories
                Bioengineering and Biotechnology
                Original Research

                locomotion,muscle synergies,motor control,treadmill locomotion,overground locomotion,fractal analysis

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