scholarly journals Muscle Synergies Reliability in the Power Clean Exercise

2020 ◽  
Vol 5 (4) ◽  
pp. 75
Author(s):  
Paulo D. G. Santos ◽  
João R. Vaz ◽  
Paulo F. Correia ◽  
Maria J. Valamatos ◽  
António P. Veloso ◽  
...  
Keyword(s):  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Human Movement ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Complex Task ◽  
Muscle Coordination ◽  
High Similarity ◽  
Strength Tests ◽  
Power Clean

Muscle synergy extraction has been utilized to investigate muscle coordination in human movement, namely in sports. The reliability of the method has been proposed, although it has not been assessed previously during a complex sportive task. Therefore, the aim of the study was to evaluate intra- and inter-day reliability of a strength training complex task, the power clean, assessing participants’ variability in the task across sets and days. Twelve unexperienced participants performed four sets of power cleans in two test days after strength tests, and muscle synergies were extracted from electromyography (EMG) data of 16 muscles. Three muscle synergies accounted for almost 90% of variance accounted for (VAF) across sets and days. Intra-day VAF, muscle synergy vectors, synergy activation coefficients and individual EMG profiles showed high similarity values. Inter-day muscle synergy vectors had moderate similarity, while the variables regarding temporal activation were still strongly related. The present findings revealed that the muscle synergies extracted during the power clean remained stable across sets and days in unexperienced participants. Thus, the mathematical procedure for the extraction of muscle synergies through nonnegative matrix factorization (NMF) may be considered a reliable method to study muscle coordination adaptations from muscle strength programs.

scholarly journals Intermuscular Coordination in the Power Clean Exercise: Comparison between Olympic Weightlifters and Untrained Individuals—A Preliminary Study

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1904
Author(s):  
Paulo D. G. Santos ◽  
João R. Vaz ◽  
Paulo F. Correia ◽  
Maria J. Valamatos ◽  
António P. Veloso ◽  
...  
Keyword(s):  
Human Movement ◽  
Muscle Synergy ◽  
Muscle Coordination ◽  
Strong Correlations ◽  
Activation Patterns ◽  
Sports Science ◽  
Training Exercises ◽  
Limb Flexion ◽  
Power Clean ◽  
Hip Extension

Muscle coordination in human movement has been assessed through muscle synergy analysis. In sports science, this procedure has been mainly applied to the comparison between highly trained and unexperienced participants. However, the lack of knowledge regarding strength training exercises led us to study the differences in neural strategies to perform the power clean between weightlifters and untrained individuals. Synergies were extracted from electromyograms of 16 muscles of ten unexperienced participants and seven weightlifters. To evaluate differences, we determined the pairwise correlations for the synergy components and electromyographic profiles. While the shape of activation patterns presented strong correlations across participants of each group, the weightings of each muscle were more variable. The three extracted synergies were shifted in time with the unexperienced group anticipating synergy #1 (−2.46 ± 18.7%; p < 0.001) and #2 (−4.60 ± 5.71%; p < 0.001) and delaying synergy #3 (1.86 ± 17.39%; p = 0.01). Moreover, muscle vectors presented more inter-group variability, changing the composition of synergy #1 and #3. These results may indicate an adaptation in intermuscular coordination with training, and athletes in an initial phase of training should attempt to delay the hip extension (synergy #1), as well as the upper-limb flexion (synergy #2).

Effects of Varying Overground Walking Speeds on Lower-Extremity Muscle Synergies in Healthy Individuals

Motor Control ◽  
2020 ◽  
pp. 1-18
Author(s):  
Manuel J. Escalona ◽  
Daniel Bourbonnais ◽  
Michel Goyette ◽  
Damien Le Flem ◽  
Cyril Duclos ◽  
...  
Keyword(s):  
Lower Extremity ◽  
Nonnegative Matrix Factorization ◽  
Walking Speed ◽  
Nonnegative Matrix ◽  
Gait Training ◽  
Muscle Synergies ◽  
Training Strategy ◽  
Lower Extremity Muscle ◽  
Walking Speeds ◽  
Weight Acceptance

The effects of walking speeds on lower-extremity muscle synergies (MSs) were investigated among 20 adults who walked 20 m at SLOW (0.6 ± 0.2 m/s), natural (NAT; 1.4 ± 0.1 m/s), and FAST (1.9 ± 0.1 m/s) speeds. Surface electromyography of eight lower-extremity muscles was recorded before extracting MSs using a nonnegative matrix factorization algorithm. Increasing walking speed tended to merge MSs associated with weight acceptance and limb deceleration, whereas reducing walking speed does not change the number and composition of MSs. Varying gait speed, particularly decreasing speed, may represent a gait training strategy needing additional attention given its effects on MSs.

scholarly journals Sensorimotor feedback based on task-relevant error robustly predicts temporal recruitment and multidirectional tuning of muscle synergies

2013 ◽  
Vol 109 (1) ◽  
pp. 31-45 ◽  
Author(s):  
Seyed A. Safavynia ◽  
Lena H. Ting
Keyword(s):  
Sagittal Plane ◽  
Balance Control ◽  
Center Of Mass ◽  
The Body ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Muscle Coordination ◽  
Initial State ◽  
Sensorimotor Feedback ◽  
Task Level

We hypothesized that motor outputs are hierarchically organized such that descending temporal commands based on desired task-level goals flexibly recruit muscle synergies that specify the spatial patterns of muscle coordination that allow the task to be achieved. According to this hypothesis, it should be possible to predict the patterns of muscle synergy recruitment based on task-level goals. We demonstrated that the temporal recruitment of muscle synergies during standing balance control was robustly predicted across multiple perturbation directions based on delayed sensorimotor feedback of center of mass (CoM) kinematics (displacement, velocity, and acceleration). The modulation of a muscle synergy's recruitment amplitude across perturbation directions was predicted by the projection of CoM kinematic variables along the preferred tuning direction(s), generating cosine tuning functions. Moreover, these findings were robust in biphasic perturbations that initially imposed a perturbation in the sagittal plane and then, before sagittal balance was recovered, perturbed the body in multiple directions. Therefore, biphasic perturbations caused the initial state of the CoM to differ from the desired state, and muscle synergy recruitment was predicted based on the error between the actual and desired upright state of the CoM. These results demonstrate that that temporal motor commands to muscle synergies reflect task-relevant error as opposed to sensory inflow. The proposed hierarchical framework may represent a common principle of motor control across motor tasks and levels of the nervous system, allowing motor intentions to be transformed into motor actions.

The flexible recruitment of muscle synergies depends on the required force-generating capability

2014 ◽  
Vol 112 (2) ◽  
pp. 316-327 ◽  
Author(s):  
Shota Hagio ◽  
Motoki Kouzaki
Keyword(s):  
Central Nervous System ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Isometric Force ◽  
Nonnegative Matrix ◽  
Muscle Synergies ◽  
Joint Angles ◽  
Activation Patterns ◽  
The Central Nervous System ◽  
Low Dimensional

To simplify redundant motor control, the central nervous system (CNS) may modularly organize and recruit groups of muscles as “muscle synergies.” However, smooth and efficient movements are expected to require not only low-dimensional organization, but also flexibility in the recruitment or combination of synergies, depending on force-generating capability of individual muscles. In this study, we examined how the CNS controls activations of muscle synergies as changing joint angles. Subjects performed multidirectional isometric force generations around right ankle and extracted the muscle synergies using nonnegative matrix factorization across various knee and hip joint angles. As a result, muscle synergies were selectively recruited with merging or decomposition as changing the joint angles. Moreover, the activation profiles, including activation levels and the direction indicating the peak, of muscle synergies across force directions depended on the joint angles. Therefore, we suggested that the CNS selects appropriate muscle synergies and controls their activation patterns based on the force-generating capability of muscles with merging or decomposing descending neural inputs.

scholarly journals Matrix Factorization Algorithms for the Identification of Muscle Synergies: Evaluation on Simulated and Experimental Data Sets

2006 ◽  
Vol 95 (4) ◽  
pp. 2199-2212 ◽  
Author(s):  
Matthew C. Tresch ◽  
Vincent C. K. Cheung ◽  
Andrea d'Avella
Keyword(s):  
Matrix Factorization ◽  
Muscle Activation ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Muscle Synergies ◽  
Data Sets ◽  
Data Set ◽  
Activation Patterns ◽  
Muscle Activation Patterns ◽  
Factorization Algorithms

Several recent studies have used matrix factorization algorithms to assess the hypothesis that behaviors might be produced through the combination of a small number of muscle synergies. Although generally agreeing in their basic conclusions, these studies have used a range of different algorithms, making their interpretation and integration difficult. We therefore compared the performance of these different algorithms on both simulated and experimental data sets. We focused on the ability of these algorithms to identify the set of synergies underlying a data set. All data sets consisted of nonnegative values, reflecting the nonnegative data of muscle activation patterns. We found that the performance of principal component analysis (PCA) was generally lower than that of the other algorithms in identifying muscle synergies. Factor analysis (FA) with varimax rotation was better than PCA, and was generally at the same levels as independent component analysis (ICA) and nonnegative matrix factorization (NMF). ICA performed very well on data sets corrupted by constant variance Gaussian noise, but was impaired on data sets with signal-dependent noise and when synergy activation coefficients were correlated. Nonnegative matrix factorization (NMF) performed similarly to ICA and FA on data sets with signal-dependent noise and was generally robust across data sets. The best algorithms were ICA applied to the subspace defined by PCA (ICAPCA) and a version of probabilistic ICA with nonnegativity constraints (pICA). We also evaluated some commonly used criteria to identify the number of synergies underlying a data set, finding that only likelihood ratios based on factor analysis identified the correct number of synergies for data sets with signal-dependent noise in some cases. We then proposed an ad hoc procedure, finding that it was able to identify the correct number in a larger number of cases. Finally, we applied these methods to an experimentally obtained data set. The best performing algorithms (FA, ICA, NMF, ICAPCA, pICA) identified synergies very similar to one another. Based on these results, we discuss guidelines for using factorization algorithms to analyze muscle activation patterns. More generally, the ability of several algorithms to identify the correct muscle synergies and activation coefficients in simulated data, combined with their consistency when applied to physiological data sets, suggests that the muscle synergies found by a particular algorithm are not an artifact of that algorithm, but reflect basic aspects of the organization of muscle activation patterns underlying behaviors.

scholarly journals Subject-Specific Muscle Synergies in Human Balance Control Are Consistent Across Different Biomechanical Contexts

2010 ◽  
Vol 103 (6) ◽  
pp. 3084-3098 ◽  
Author(s):  
Gelsy Torres-Oviedo ◽  
Lena H. Ting
Keyword(s):  
Muscle Activation ◽  
Balance Control ◽  
Nonnegative Matrix ◽  
The Body ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Support Surface ◽  
Activation Patterns ◽  
Muscle Activation Patterns ◽  
Specific Muscle

The musculoskeletal redundancy of the body provides multiple solutions for performing motor tasks. We have proposed that the nervous system solves this unconstrained problem through the recruitment of motor modules or functional muscle synergies that map motor intention to action. Consistent with this hypothesis, we showed that trial-by-trial variations in muscle activation for multidirectional balance control in humans were constrained by a small set of muscle synergies. However, apparent muscle synergy structures could arise from characteristic patterns of sensory input resulting from perturbations or from low-dimensional optimal motor solutions. Here we studied electromyographic (EMG) responses for balance control across a range of biomechanical contexts, which alter not only the sensory inflow generated by postural perturbations, but also the muscle activation patterns used to restore balance. Support-surface translations in 12 directions were delivered to subjects standing in six different postural configurations: one-leg, narrow, wide, very wide, crouched, and normal stance. Muscle synergies were extracted from each condition using nonnegative matrix factorization. In addition, muscle synergies from the normal stance condition were used to reconstruct muscle activation patterns across all stance conditions. A consistent set of muscle synergies were recruited by each subject across conditions. When balance demands were extremely different from the normal stance (e.g., one-legged or crouched stance), task-specific muscle synergies were recruited in addition to the preexisting ones, rather generating de novo muscle synergies. Taken together, our results suggest that muscle synergies represent consistent motor modules that map intention to action, regardless of the biomechanical context of the task.

scholarly journals Modular Organization of Muscle Synergies to Achieve Movement Behaviors

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Kunkun Zhao ◽  
Zhisheng Zhang ◽  
Haiying Wen ◽  
Zihan Wang ◽  
Jiankang Wu
Keyword(s):  
Motion Control ◽  
Control Mechanism ◽  
Nonnegative Matrix ◽  
Movement Patterns ◽  
Motor Dysfunction ◽  
Muscle Synergy ◽  
Modular Organization ◽  
Muscle Synergies ◽  
Motion Primitives ◽  
Motion Control Mechanism

Muscle synergy has been applied to comprehend how the central nervous system (CNS) controls movements for decades. However, it is not clear about the motion control mechanism and the relationship between motions and muscle synergies. In this paper, we designed two experiments to corroborate the hypothesis: (1) motions can be decomposed to motion primitives, which are driven by muscle synergy primitives and (2) variations of motion primitives in direction and scale are modulated by activation coefficients rather than muscle synergy primitives. Surface electromyographic (EMG) signals were recorded from nine muscles of the upper limb. Nonnegative matrix factorization (NMF) was applied to extract muscle synergy vectors and corresponding activation coefficients. We found that synergy structures of different movement patterns were similar (α=0.05). The motion modulation indexes (MMI) among movement patterns in reaching movements showed apparent differences. Merging coefficients and reconstructed similarity of synergies between simple motions and complex motions were significant. This study revealed the motion control mechanism of the CNS and provided a rehabilitation and evaluation method for patients with motor dysfunction in exercise and neuroscience.

scholarly journals On the Reliability and Repeatability of Surface Electromyography Factorization by Muscle Synergies in Daily Life Activities

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Juri Taborri ◽  
Eduardo Palermo ◽  
Zaccaria Del Prete ◽  
Stefano Rossi
Keyword(s):  
Activity Patterns ◽  
Intraclass Correlation ◽  
Nonnegative Matrix ◽  
Cosine Similarity ◽  
Muscle Synergy ◽  
Coefficient Of Determination ◽  
Muscle Synergies ◽  
Daily Life Activities ◽  
Research Fields ◽  
Emg Signal

Muscle synergy theory is a new appealing approach for different research fields. This study is aimed at evaluating the robustness of EMG reconstruction via muscle synergies and the repeatability of muscle synergy parameters as potential neurophysiological indices. Eight healthy subjects performed walking, stepping, running, and ascending and descending stairs’ trials for five repetitions in three sessions. Twelve muscles of the dominant leg were analyzed. The “nonnegative matrix factorization” and “variability account for” were used to extract muscle synergies and to assess EMG goodness reconstruction, respectively. Intraclass correlation was used to quantify methodology reliability. Cosine similarity and coefficient of determination assessed the repeatability of the muscle synergy vectors and the temporal activity patterns, respectively. A 4-synergy model was selected for EMG signal factorization. Intraclass correlation was excellent for the overall reconstruction, while it ranged from fair to excellent for single muscles. The EMG reconstruction was found repeatable across sessions and subjects. Considering the selection of neurophysiological indices, the number of synergies was not repeatable neither within nor between subjects. Conversely, the cosine similarity and coefficient of determination values allow considering the muscle synergy vectors and the temporal activity patterns as potential neurophysiological indices due to their similarity both within and between subjects. More specifically, some synergies in the 4-synergy model reveal themselves as more repeatable than others, suggesting focusing on them when seeking at the neurophysiological index identification.

scholarly journals Can Measured Synergy Excitations Accurately Construct Unmeasured Muscle Excitations?

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Nicholas A. Bianco ◽  
Carolynn Patten ◽  
Benjamin J. Fregly
Keyword(s):  
Muscle Force ◽  
Human Movement ◽  
Optimization Methods ◽  
Contact Forces ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Large Set ◽  
Dimensional Representation ◽  
Joint Contact ◽  
Low Dimensional

Accurate prediction of muscle and joint contact forces during human movement could improve treatment planning for disorders such as osteoarthritis, stroke, Parkinson's disease, and cerebral palsy. Recent studies suggest that muscle synergies, a low-dimensional representation of a large set of muscle electromyographic (EMG) signals (henceforth called “muscle excitations”), may reduce the redundancy of muscle excitation solutions predicted by optimization methods. This study explores the feasibility of using muscle synergy information extracted from eight muscle EMG signals (henceforth called “included” muscle excitations) to accurately construct muscle excitations from up to 16 additional EMG signals (henceforth called “excluded” muscle excitations). Using treadmill walking data collected at multiple speeds from two subjects (one healthy, one poststroke), we performed muscle synergy analysis on all possible subsets of eight included muscle excitations and evaluated how well the calculated time-varying synergy excitations could construct the remaining excluded muscle excitations (henceforth called “synergy extrapolation”). We found that some, but not all, eight-muscle subsets yielded synergy excitations that achieved >90% extrapolation variance accounted for (VAF). Using the top 10% of subsets, we developed muscle selection heuristics to identify included muscle combinations whose synergy excitations achieved high extrapolation accuracy. For 3, 4, and 5 synergies, these heuristics yielded extrapolation VAF values approximately 5% lower than corresponding reconstruction VAF values for each associated eight-muscle subset. These results suggest that synergy excitations obtained from experimentally measured muscle excitations can accurately construct unmeasured muscle excitations, which could help limit muscle excitations predicted by muscle force optimizations.

scholarly journals Effect of acute noxious stimulation to the leg or back on muscle synergies during walking

2015 ◽  
Vol 113 (1) ◽  
pp. 244-254 ◽  
Author(s):  
Wolbert van den Hoorn ◽  
Paul W. Hodges ◽  
Jaap H. van Dieën ◽  
François Hug
Keyword(s):  
Vastus Lateralis ◽  
Plantar Flexion ◽  
Rectus Femoris ◽  
Muscle Synergy ◽  
Noxious Stimulation ◽  
Muscle Synergies ◽  
Muscle Coordination ◽  
Painful Condition ◽  
Leg Muscles ◽  
Pain Location

This study aimed to examine how acute muscle pain affects muscle coordination during gait with consideration of muscle synergies (i.e., group of muscles activated in synchrony), amplitude of muscle activity and kinematics. A secondary aim was to determine whether any adaptation was specific to pain location. Sixteen participants walked on a treadmill during 5 conditions [control, low back pain (LBP), washout LBP, calf pain (CalfP), and washout CalfP]. Five muscle synergies were identified for all of the conditions. Cross-validation analysis showed that muscle synergy vectors extracted for the control condition accounted for >81% of variance accounted for from the other conditions. Muscle synergies were altered very little in some participants ( n = 7 for LBP; n = 10 for CalfP), but were more affected in the others ( n = 9 for LBP; n = 6 for CalfP). No systematic differences between pain locations were observed. Considering all participants, synergies related to propulsion and weight acceptance were largely unaffected by pain, whereas synergies related to other functions (trunk control and leg deceleration) were more affected. Gastrocnemii activity was less during both CalfP and LBP than control. Soleus activity was further reduced during CalfP, and this was associated with reduced plantar flexion. Some lower leg muscles exhibited adaptations depending on pain location (e.g., greater vastus lateralis and rectus femoris activity during CalfP than LBP). Overall, these changes in muscle coordination involve a participant-specific strategy that is important to further explore, as it may explain why some people are more likely to develop persistence of a painful condition.

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