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 (rmax) 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, rmax-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.
Highlights
Understanding how the central nervous system controls movement of the human body is a challenging question due to the biomechanical redundancy of the neuromusculoskeletal system, which is referred to as Bernstein’s degrees of freedom problem (Bernstein, 1967)
In line with this latter proposition, it has been shown during both postural (Torres-Oviedo and Ting, 2010) and locomotor tasks (Hug et al, 2011; Chvatal and Ting, 2012) that muscle synergy vectors are robust across various mechanical constraints allowing the temporal recruitment to vary according to the task demand
Did the learning process necessary to perform this task led to similar muscle synergies or did each individual develop specific synergies related to their personal anthropometric, anatomical, or muscular characteristics? In order to answer these questions, we looked at a homogeneous population of nine experienced gymnasts performing giant swings on a high bar
Summary
Understanding how the central nervous system controls movement of the human body is a challenging question due to the biomechanical redundancy of the neuromusculoskeletal system, which is referred to as Bernstein’s degrees of freedom problem (Bernstein, 1967). The decomposition of multiple surface electromyographic (EMG) signals can be used to extract these synergies This decomposition algorithm is based on two components: “muscle synergy vectors” which represent the relative weighting of each muscle within each synergy; and a “synergy activation coefficient” which represents the recruitment of the muscle synergy over time (Torres-Oviedo and Ting, 2007; Hug et al, 2011). Others have suggested that the muscle synergies are spatially fixed (i.e., muscle weightings are invariant) across subjects/test conditions while temporal recruitment patterns can change (Saltiel et al, 2001; Hart and Giszter, 2004; Torres-Oviedo and Ting, 2007; Hug et al, 2011; Safavynia and Ting, 2012). Altering the recruitment pattern of spatially fixed muscle synergies can produce different motor behaviors in animals (Cheung et al, 2005; Kargo et al, 2010)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
