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      State-space intermittent feedback stabilization of a dual balancing task

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          Abstract

          Balancing the body in upright standing and balancing a stick on the fingertip are two examples of unstable tasks that, in spite of strong motor and sensory differences, appear to share a similar motor control paradigm, namely a state-space intermittent feedback stabilization mechanism. In this study subjects were required to perform the two tasks simultaneously, with the purpose of highlighting both the coordination between the two skills and the underlying interaction between the corresponding controllers. The experimental results reveal, in particular, that upright standing (the less critical task) is modified in an adaptive way, in order to facilitate the more critical task (stick balancing), but keeping the overall spatio-temporal signature well known in regular upright standing. We were then faced with the following question: to which extent the physical/biomechanical interaction between the two independent intermittent controllers is capable to explain the dual task coordination patterns, without the need to introduce an additional, supervisory layer/module? By comparing the experimental data with the output of a simulation study we support the former hypothesis, suggesting that it is made possible by the intrinsic robustness of both state-space intermittent feedback stabilization mechanisms.

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          Most cited references35

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          Movement, posture and equilibrium: interaction and coordination.

          J Massion (1991)
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            Random walking during quiet standing.

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              Intermittent control: a computational theory of human control.

              The paradigm of continuous control using internal models has advanced understanding of human motor control. However, this paradigm ignores some aspects of human control, including intermittent feedback, serial ballistic control, triggered responses and refractory periods. It is shown that event-driven intermittent control provides a framework to explain the behaviour of the human operator under a wider range of conditions than continuous control. Continuous control is included as a special case, but sampling, system matched hold, an intermittent predictor and an event trigger allow serial open-loop trajectories using intermittent feedback. The implementation here may be described as "continuous observation, intermittent action". Beyond explaining unimodal regulation distributions in common with continuous control, these features naturally explain refractoriness and bimodal stabilisation distributions observed in double stimulus tracking experiments and quiet standing, respectively. Moreover, given that human control systems contain significant time delays, a biological-cybernetic rationale favours intermittent over continuous control: intermittent predictive control is computationally less demanding than continuous predictive control. A standard continuous-time predictive control model of the human operator is used as the underlying design method for an event-driven intermittent controller. It is shown that when event thresholds are small and sampling is regular, the intermittent controller can masquerade as the underlying continuous-time controller and thus, under these conditions, the continuous-time and intermittent controller cannot be distinguished. This explains why the intermittent control hypothesis is consistent with the continuous control hypothesis for certain experimental conditions.
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                Author and article information

                Contributors
                pietro.morasso@iit.it
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                21 May 2020
                21 May 2020
                2020
                : 10
                : 8470
                Affiliations
                [1 ]ISNI 0000 0004 1764 2907, GRID grid.25786.3e, Department of Robotics, Brain and Cognitive Sciences, , Istituto Italiano di Tecnologia, ; Via Enrico Melen 83, 16152 Genoa, Italy
                [2 ]ISNI 0000 0001 2151 3065, GRID grid.5606.5, Department of Informatics, Bioengineering, Robotics, and System Engineering, , University of Genoa, ; Via all’Opera Pia 13, 16145 Genoa, Italy
                Article
                64911
                10.1038/s41598-020-64911-7
                7242428
                32439947
                2cd55a46-cd26-4ce0-805a-e2ed00425d0c
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 November 2019
                : 13 March 2020
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                © The Author(s) 2020

                Uncategorized
                computational neuroscience,motor control,sensorimotor processing
                Uncategorized
                computational neuroscience, motor control, sensorimotor processing

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