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      Between-subject variability of muscle synergies during a complex motor skill

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          Abstract

          The purpose of the present study was to determine whether subjects who have learned a complex motor skill exhibit similar neuromuscular control strategies. We studied a population of experienced gymnasts during backward giant swings on the high bar. This cyclic movement is interesting because it requires learning, as untrained subjects are unable to perform this task. Nine gymnasts were tested. Both kinematics and electromyographic (EMG) patterns of 12 upper-limb and trunk muscles were recorded. Muscle synergies were extracted by non-negative matrix factorization (NMF), providing two components: muscle synergy vectors and synergy activation coefficients. First, the coefficient of correlation ( r) and circular cross-correlation ( r max) were calculated to assess similarities in the mechanical patterns, EMG patterns, and muscle synergies between gymnasts. We performed a further analysis to verify that the muscle synergies (in terms of muscle synergy vectors or synergy activation coefficients) extracted for one gymnast accounted for the EMG patterns of the other gymnasts. Three muscle synergies explained 89.9 ± 2.0% of the variance accounted for (VAF). The coefficients of correlation of the muscle synergy vectors among the participants were 0.83 ± 0.08, 0.86 ± 0.09, and 0.66 ± 0.28 for synergy #1, #2, and #3, respectively. By keeping the muscle synergy vectors constant, we obtained an averaged VAF across all pairwise comparisons of 79 ± 4%. For the synergy activation coefficients, r max-values were 0.96 ± 0.03, 0.92 ± 0.03, and 0.95 ± 0.03, for synergy #1, #2, and #3, respectively. By keeping the synergy activation coefficients constant, we obtained an averaged VAF across all pairwise comparisons of 72 ± 5%. Although variability was found (especially for synergy #3), the gymnasts exhibited gross similar neuromuscular strategies when performing backward giant swings. This confirms that the muscle synergies are consistent across participants, even during a skilled motor task that requires learning.

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          Most cited references 53

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          Development of recommendations for SEMG sensors and sensor placement procedures.

           H Hermens,  C Klug,  G. C. Rau (2000)
          The knowledge of surface electromyography (SEMG) and the number of applications have increased considerably during the past ten years. However, most methodological developments have taken place locally, resulting in different methodologies among the different groups of users.A specific objective of the European concerted action SENIAM (surface EMG for a non-invasive assessment of muscles) was, besides creating more collaboration among the various European groups, to develop recommendations on sensors, sensor placement, signal processing and modeling. This paper will present the process and the results of the development of the recommendations for the SEMG sensors and sensor placement procedures. Execution of the SENIAM sensor tasks, in the period 1996-1999, has been handled in a number of partly parallel and partly sequential activities. A literature scan was carried out on the use of sensors and sensor placement procedures in European laboratories. In total, 144 peer-reviewed papers were scanned on the applied SEMG sensor properties and sensor placement procedures. This showed a large variability of methodology as well as a rather insufficient description. A special workshop provided an overview on the scientific and clinical knowledge of the effects of sensor properties and sensor placement procedures on the SEMG characteristics. Based on the inventory, the results of the topical workshop and generally accepted state-of-the-art knowledge, a first proposal for sensors and sensor placement procedures was defined. Besides containing a general procedure and recommendations for sensor placement, this was worked out in detail for 27 different muscles. This proposal was evaluated in several European laboratories with respect to technical and practical aspects and also sent to all members of the SENIAM club (>100 members) together with a questionnaire to obtain their comments. Based on this evaluation the final recommendations of SENIAM were made and published (SENIAM 8: European recommendations for surface electromyography, 1999), both as a booklet and as a CD-ROM. In this way a common body of knowledge has been created on SEMG sensors and sensor placement properties as well as practical guidelines for the proper use of SEMG.
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            Adjustments to Zatsiorsky-Seluyanov's segment inertia parameters.

            Zatsiorsky et al. (in Contemporary Problems in Biomechanics, pp. 272-291, CRC Press, Massachusetts, 1990a) obtained, by means of a gamma-ray scanning technique, the relative body segment masses, center of mass (CM) positions, and radii of gyration for samples of college-aged Caucasian males and females. Although these data are the only available and comprehensive set of inertial parameters regarding young adult Caucasians, they have been rarely utilized for biomechanical analyses of subjects belonging to the same or a similar population. The main reason is probably that Zatsiorsky et al. used bony landmarks as reference points for locating segment CMs and defining segment lengths. Some of these landmarks were markedly distant from the joint centers currently used by most researchers as reference points. The purpose of this study was to adjust the mean relative CM positions and radii of gyration reported by Zatsiorsky et al., in order to reference them to the joint centers or other commonly used landmarks, rather than the original landmarks. The adjustments were based on a number of carefully selected sources of anthropometric data.
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              Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke.

              Evidence suggests that the nervous system controls motor tasks using a low-dimensional modular organization of muscle activation. However, it is not clear if such an organization applies to coordination of human walking, nor how nervous system injury may alter the organization of motor modules and their biomechanical outputs. We first tested the hypothesis that muscle activation patterns during walking are produced through the variable activation of a small set of motor modules. In 20 healthy control subjects, EMG signals from eight leg muscles were measured across a range of walking speeds. Four motor modules identified through nonnegative matrix factorization were sufficient to account for variability of muscle activation from step to step and across speeds. Next, consistent with the clinical notion of abnormal limb flexion-extension synergies post-stroke, we tested the hypothesis that subjects with post-stroke hemiparesis would have altered motor modules, leading to impaired walking performance. In post-stroke subjects (n = 55), a less complex coordination pattern was shown. Fewer modules were needed to account for muscle activation during walking at preferred speed compared with controls. Fewer modules resulted from merging of the modules observed in healthy controls, suggesting reduced independence of neural control signals. The number of modules was correlated to preferred walking speed, speed modulation, step length asymmetry, and propulsive asymmetry. Our results suggest a common modular organization of muscle coordination underlying walking in both healthy and post-stroke subjects. Identification of motor modules may lead to new insight into impaired locomotor coordination and the underlying neural systems.
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                Author and article information

                Journal
                Front Comput Neurosci
                Front Comput Neurosci
                Front. Comput. Neurosci.
                Frontiers in Computational Neuroscience
                Frontiers Media S.A.
                1662-5188
                28 December 2012
                2012
                : 6
                Affiliations
                1Laboratory « Motricité, Interactions, Performance », University of Maine Le Mans, France
                2Laboratory « Motricité, Interactions, Performance », University of Nantes Nantes, France
                Author notes

                Edited by: Andrea D'Avella, IRCCS Fondazione Santa Lucia, Italy

                Reviewed by: Vincent C. K. Cheung, Massachusetts Institute of Technology, USA; Gelsy Torres-Oviedo, University of Pittsburgh, USA

                *Correspondence: François Hug, Laboratory « Motricité, Interactions, Performance » (EA 4334), University of Nantes, 25 bis boulevard Guy Mollet, BP 72206, 44322 Nantes cedex 3, France. e-mail: francois.hug@ 123456univ-nantes.fr
                Article
                10.3389/fncom.2012.00099
                3531715
                23293599
                Copyright © 2012 Frère and Hug.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                Page count
                Figures: 8, Tables: 2, Equations: 7, References: 57, Pages: 13, Words: 10135
                Categories
                Neuroscience
                Original Research Article

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