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 Compensation or Recovery? Altered Kinetics and Neuromuscular Synergies Following High-Intensity Stepping Training Poststroke

2018 ◽  
Vol 33 (1) ◽  
pp. 47-58 ◽  
Author(s):  
Marzieh M. Ardestani ◽  
Catherine R. Kinnaird ◽  
Christopher E. Henderson ◽  
T. George Hornby
Keyword(s):  
High Intensity ◽  
Nonnegative Matrix Factorization ◽  
Walking Speed ◽  
Nonnegative Matrix ◽  
Emg Activity ◽  
Muscle Synergies ◽  
Joint Kinetics ◽  
Compensation Methods ◽  
Movement Strategies ◽  
Neuromuscular Coordination

Background. High-intensity, variable stepping training can improve walking speed in individuals poststroke, although neuromuscular strategies used to achieve faster speeds are unclear. We evaluated changes in joint kinetics and neuromuscular coordination following such training; movement strategies consistent with intact individuals were considered evidence of recovery and abnormal strategies indicative of compensation. Methods. A total of 15 individuals with stroke (duration: 23 ± 30 months) received ≤40 sessions of high-intensity stepping in variable contexts (tasks and environments). Lower-extremity kinetics and electromyographic (EMG) activity were collected prior to (BSL) and following (POST) training at peak treadmill speeds and speeds matched to peak BSL (MATCH). Primary measures included positive (concentric) joint and total limb powers, measures of interlimb (paretic/nonparetic powers) and intralimb compensation (hip/ankle or knee/ankle powers), and muscle synergies calculated using nonnegative matrix factorization. Results. Gains in most positive paretic and nonparetic joint powers were observed at higher speeds at POST, with decreased interlimb compensation and limited changes in intralimb compensation. There were very few differences in kinetic measures between BSL to MATCH conditions. However, the number of neuromuscular synergies increased significantly following training at both POST and MATCH conditions, indicating gains from training rather than altered speeds. Despite these results, speed improvements were associated primarily with changes in nonparetic versus paretic powers. Conclusion. Gains in locomotor function were accomplished by movement strategies consistent with both recovery and compensation. These and other data indicate that both strategies may be necessary to maximize walking function in patients poststroke.

scholarly journals Modulation of spatial and temporal modules in lower limb muscle activations during walking with simulated reduced gravity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shota Hagio ◽  
Makoto Nakazato ◽  
Motoki Kouzaki
Keyword(s):  
Matrix Factorization ◽  
Lower Limb ◽  
Nonnegative Matrix Factorization ◽  
Walking Speed ◽  
Nonnegative Matrix ◽  
Tensor Decomposition ◽  
Reduced Gravity ◽  
Dependent Variables ◽  
Specific Speed ◽  
Muscle Activations

AbstractGravity plays a crucial role in shaping patterned locomotor output to maintain dynamic stability during locomotion. The present study aimed to clarify the gravity-dependent regulation of modules that organize multiple muscle activities during walking in humans. Participants walked on a treadmill at seven speeds (1–6 km h−1 and a subject- and gravity-specific speed determined by the Froude number (Fr) corresponding to 0.25) while their body weight was partially supported by a lift to simulate walking with five levels of gravity conditions from 0.07 to 1 g. Modules, i.e., muscle-weighting vectors (spatial modules) and phase-dependent activation coefficients (temporal modules), were extracted from 12 lower-limb electromyographic (EMG) activities in each gravity (Fr ~ 0.25) using nonnegative matrix factorization. Additionally, a tensor decomposition model was fit to the EMG data to quantify variables depending on the gravity conditions and walking speed with prescribed spatial and temporal modules. The results demonstrated that muscle activity could be explained by four modules from 1 to 0.16 g and three modules at 0.07 g, and the modules were shared for both spatial and temporal components among the gravity conditions. The task-dependent variables of the modules acting on the supporting phase linearly decreased with decreasing gravity, whereas that of the module contributing to activation prior to foot contact showed nonlinear U-shaped modulation. Moreover, the profiles of the gravity-dependent modulation changed as a function of walking speed. In conclusion, reduced gravity walking was achieved by regulating the contribution of prescribed spatial and temporal coordination in muscle activities.

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.

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 Wearable Biofeedback System to Induce Desired Walking Speed in Overground Gait Training

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4002
Author(s):  
Huanghe Zhang ◽  
Yefei Yin ◽  
Zhuo Chen ◽  
Yufeng Zhang ◽  
Ashwini K. Rao ◽  
...  
Keyword(s):  
Closed Loop ◽  
Walking Speed ◽  
New Technology ◽  
Past Research ◽  
Gait Training ◽  
Open Loop ◽  
Vibrotactile Stimulation ◽  
Loop Control ◽  
Biofeedback System ◽  
Walking Speeds

Biofeedback systems have been extensively used in walking exercises for gait improvement. Past research has focused on modulating the wearer’s cadence, gait variability, or symmetry, but none of the previous works has addressed the problem of inducing a desired walking speed in the wearer. In this paper, we present a new, minimally obtrusive wearable biofeedback system (WBS) that uses closed-loop vibrotactile control to elicit desired changes in the wearer’s walking speed, based on the predicted user response to anticipatory and delayed feedback. The performance of the proposed control was compared to conventional open-loop rhythmic vibrotactile stimulation with N = 10 healthy individuals who were asked to complete a set of walking tasks along an oval path. The closed-loop vibrotactile control consistently demonstrated better performance than the open-loop control in inducing desired changes in the wearer’s walking speed, both with constant and with time-varying target walking speeds. Neither open-loop nor closed-loop stimuli affected natural gait significantly, when the target walking speed was set to the individual’s preferred walking speed. Given the importance of walking speed as a summary indicator of health and physical performance, the closed-loop vibrotactile control can pave the way for new technology-enhanced protocols for gait rehabilitation.

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