ORCID Profile
0000-0001-7046-110X
Current Organisation
Université de la Réunion
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Publisher: The Company of Biologists
Date: 10-2011
DOI: 10.1242/JEB.057133
Abstract: Muscle fatigue is an exercise-induced reduction in the capability of a muscle to generate force. A possible strategy to counteract the effects of fatigue is to modify muscle coordination. We designed this study to quantify the effect of fatigue on muscle coordination during a cyclic exercise involving numerous muscles. Nine human subjects were tested during a constant-load rowing exercise (mean power output: 217.9±32.4 W) performed until task failure. The forces exerted at the handle and the foot-stretcher were measured continuously and were synchronized with surface electromyographic (EMG) signals measured in 23 muscles. In addition to a classical analysis of in idual EMG data (EMG profile and EMG activity level), a non-negative matrix factorization algorithm was used to identify the muscle synergies at the start and the end of the test. Among the 23 muscles tested, 16 showed no change in their mean activity level across the rowing cycle, five (biceps femoris, gluteus maximus, semitendinosus, trapezius medius and vastus medialis) showed a significant increase and two (gastrocnemius lateralis and longissimus) showed a significant decrease. We found no change in the number of synergies during the fatiguing test, i.e. three synergies accounted for more than 90% of variance accounted for at the start (92.4±1.5%) and at the end (91.0±1.8%) of the exercise. Very slight modifications at the level of in idual EMG profiles, synergy activation coefficients and muscle synergy vectors were observed. These results suggest that fatigue during a cyclic task preferentially induces an adaptation in muscle activity level rather than changes in the modular organization of the muscle coordination.
Publisher: American Physiological Society
Date: 07-2011
Abstract: The purpose of the present study was to determine whether muscle synergies are constrained by changes in the mechanics of pedaling. The decomposition algorithm used to identify muscle synergies was based on two components: “muscle synergy vectors,” which represent the relative weighting of each muscle within each synergy, and “synergy activation coefficients,” which represent the relative contribution of muscle synergy to the overall muscle activity pattern. We hypothesized that muscle synergy vectors would remain fixed but that synergy activation coefficients could vary, resulting in observed variations in in idual electromyographic (EMG) patterns. Eleven cyclists were tested during a submaximal pedaling exercise and five all-out sprints. The effects of torque, maximal torque-velocity combination, and posture were studied. First, muscle synergies were extracted from each pedaling exercise independently using non-negative matrix factorization. Then, to cross-validate the results, muscle synergies were extracted from the entire data pooled across all conditions, and muscle synergy vectors extracted from the submaximal exercise were used to reconstruct EMG patterns of the five all-out sprints. Whatever the mechanical constraints, three muscle synergies accounted for the majority of variability [mean variance accounted for (VAF) = 93.3 ± 1.6%, VAF muscle 82.5%] in the EMG signals of 11 lower limb muscles. In addition, there was a robust consistency in the muscle synergy vectors. This high similarity in the composition of the three extracted synergies was accompanied by slight adaptations in their activation coefficients in response to extreme changes in torque and posture. Thus, our results support the hypothesis that these muscle synergies reflect a neural control strategy, with only a few timing adjustments in their activation regarding the mechanical constraints.
Publisher: Springer Science and Business Media LLC
Date: 31-03-2011
DOI: 10.1007/S00421-011-1928-X
Abstract: The present study was designed to quantify the effect of power output on muscle coordination during rowing. Surface electromyographic (EMG) activity of 23 muscles and mechanical variables were recorded in eight untrained subjects and seven experienced rowers. Each subject was asked to perform three 2-min constant-load exercises performed at 60, 90 and 120% of the mean power output over a maximal 2,000-m event (denoted as P60, P90, and P120, respectively). A decomposition algorithm (nonnegative matrix factorization) was used to extract the muscle synergies that represent the global temporal and spatial organization of the motor output. The results showed a main effect of power output for 22 of 23 muscles (p values ranged from <0.0001 to 0.004) indicating a significant increase in EMG activity level with power output for both untrained and experienced subjects. However, for the two populations, no dramatic modification in the shape of in idual EMG patterns (mean r (max) value = 0.93 ± 0.09) or in their timing of activation (maximum lag time = -4.3 ± 3.8% of the rowing cycle) was found. The results also showed a large consistency of the three extracted muscle synergies, for both synergy activation coefficients (mean r (max) values range from 0.87 to 0.97) and muscle synergy vectors (mean r values range from 0.70 to 0.76) across the three power outputs. In conclusion, despite significant changes in the level of muscle activity, the global temporal and spatial organization of the motor output is very little affected by power output on a rowing ergometer.
Publisher: Elsevier BV
Date: 12-2011
DOI: 10.1016/J.JELEKIN.2011.07.013
Abstract: The purpose of the present study was to determine whether expertise in rowing is driven by a specific structure in muscular coordination. We compared seven experienced rowers and eight untrained (i.e., inexperienced) subjects during rowing on an ergometer. Both surface electromyography activity and mechanical patterns (forces exerted at the handle and the foot-stretcher) were recorded during a high intensity rowing exercise. A non-negative matrix factorization was applied to 23 electromyographic patterns to differentiate muscle synergies. Results showed that expertise was not associated with different dimensionality in the electromyographic data and that three muscle synergies were sufficient to explain the majority of the variance accounted for (i.e., >90% of the total variance) in the two populations. The synergies extracted were similar in the two populations, with identical functional roles. While the temporal organization of the propulsive synergies was very similar, slight differences were found in the composition of the muscle synergies (muscle synergy vectors) between the two populations. The results suggests that rowing expertise would not require the development of novel muscle synergies but would imply intrinsic synergies already used in different behaviors. Performance in rowing is more probably linked to adjustments in the mechanical output of the muscle synergies rather than to differences in the shape and timing of their activations.
Publisher: Elsevier BV
Date: 09-2012
Publisher: American Physiological Society
Date: 06-2010
DOI: 10.1152/JAPPLPHYSIOL.01305.2009
Abstract: Our aim was to determine whether muscle synergies are similar across trained cyclists (and thus whether the same locomotor strategies for pedaling are used), despite interin idual variability of in idual EMG patterns. Nine trained cyclists were tested during a constant-load pedaling exercise performed at 80% of maximal power. Surface EMG signals were measured in 10 lower limb muscles. A decomposition algorithm (nonnegative matrix factorization) was applied to a set of 40 consecutive pedaling cycles to differentiate muscle synergies. We selected the least number of synergies that provided 90% of the variance accounted for VAF. Using this criterion, three synergies were identified for all of the subjects, accounting for 93.5 ± 2.0% of total VAF, with VAF for in idual muscles ranging from 89.9 ± 8.2% to 96.6 ± 1.3%. Each of these synergies was quite similar across all subjects, with a high mean correlation coefficient for synergy activation coefficients (0.927 ± 0.070, 0.930 ± 0.052, and 0.877 ± 0.110 for synergies 1– 3, respectively) and muscle synergy vectors (0.873 ± 0.120, 0.948 ± 0.274, and 0.885 ± 0.129 for synergies 1– 3, respectively). Despite a large consistency across subjects in the weighting of several monoarticular muscles into muscle synergy vectors, we found larger interin idual variability for another monoarticular muscle (soleus) and for biarticular muscles (rectus femoris, gastrocnemius lateralis, biceps femoris, and semimembranosus). This study demonstrated that pedaling is accomplished by the combination of the similar three muscle synergies among trained cyclists. The interin idual variability of EMG patterns observed during pedaling does not represent differences in the locomotor strategy for pedaling.
Location: France
No related grants have been discovered for Nicolas, Alain Turpin.