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.

Muscle and Contact Contributions to Inverse Dynamic Knee Loads During Gait

2009 ◽  
Author(s):  
Benjamin J. Fregly ◽  
Yi-Chung Lin ◽  
Jonathan P. Walter ◽  
Marcus G. Pandy ◽  
Scott A. Banks ◽  
...  
Keyword(s):  
Articular Cartilage ◽  
Degrees Of Freedom ◽  
Muscle Force ◽  
Human Movement ◽  
Contact Forces ◽  
Cartilage Defects ◽  
Inverse Dynamic ◽  
Joint Replacements ◽  
Joint Contact ◽  
Articular Cartilage Defects

Musculoskeletal computer models capable of predicting muscle and joint contact forces accurately during human movement could facilitate the design of improved joint replacements and new clinical treatments for articular cartilage defects or movement-related disorders [1]. A primary challenge to developing such predictions is the non-uniqueness of the calculated muscle forces, often referred to as the “muscle redundancy problem” [2]. Since more muscles act on the skeleton than the number of degrees of freedom in the skeleton, an infinite number of possible muscle force solutions exist.

Muscle Synergy Constraints Improve Prediction of Knee Contact Force During Gait

2013 ◽  
Author(s):  
Benjamin J. Fregly ◽  
Jonathan P. Walter ◽  
Allison L. Kinney ◽  
Scott A. Banks ◽  
Darryl D. D’Lima ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Neural Control ◽  
Optimization Methods ◽  
Internal Force ◽  
Contact Forces ◽  
Muscle Synergy ◽  
Patient Specific ◽  
Joint Contact ◽  
Specific Muscle ◽  
Clinical Conditions

Knowledge of patient-specific muscle and joint contact forces during activities of daily living could improve the treatment of movement-related disorders (e.g., osteoarthritis, stroke, cerebral palsy, Parkinson’s disease). Unfortunately, it is currently impossible to measure these quantities directly under common clinical conditions, and calculation of these quantities using computer models is limited by the redundant nature of human neural control (i.e., more muscles than theoretically necessary to actuate the available degrees of freedom in the skeleton). Walking is a particularly important task to understand, since loss of mobility is associated with increased morbidity and decreased quality of life [1]. Though numerous musculoskeletal computer modeling studies have used optimization methods to resolve the neural control redundancy problem, these estimates remain unvalidated due to the lack of internal force measurements that can be used for validation purposes.

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 When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance

2019 ◽  
Author(s):  
Victor R. Barradas ◽  
Jason J. Kutch ◽  
Toshihiro Kawase ◽  
Yasuharu Koike ◽  
Nicolas Schweighofer
Keyword(s):  
Motor Control ◽  
Dimensionality Reduction ◽  
Muscle Activity ◽  
Muscle Force ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Systematic Effect ◽  
Extraction Techniques ◽  
Force Mapping ◽  
Variance Explained

AbstractMuscle synergies are usually identified via dimensionality reduction techniques, such that the identified synergies reconstruct the muscle activity to a level of accuracy defined heuristically, such as 90% of the variance explained. Here, we question the assumption that the residual muscle activity not explained by the synergies is due to noise. We hypothesize instead that the residual activity is structured and can therefore influence the execution of a motor task. Young healthy subjects performed an isometric reaching task in which surface electromyography of 10 arm muscles was mapped onto estimated two-dimensional forces used to control a cursor. Three to five synergies were extracted to account for 90% of the variance explained. We then altered the muscle-force mapping via “hard” and “easy” virtual surgeries. Whereas in both surgeries the forces associated with synergies spanned the same single dimension of the virtual environment, the muscle-force mapping was as close as possible to the initial mapping in the easy surgery and as far as possible in the hard surgery. This design therefore maximized potential differences in reaching errors attributable to the residual muscle activity. Results show that the easy surgery produced much smaller directional errors than the hard task. In addition, systematic estimations of the errors for easy and hard surgeries constructed with 1 to 10 synergies show that the errors differ significantly for up to 8 synergies, which account for 98% of the variance on average. Our study therefore indicates the need for cautious interpretations of results derived from synergy extraction techniques based on heuristics with lenient levels of accuracy.Author summaryThe muscle synergy hypothesis states that the central nervous system simplifies motor control by grouping muscles that share common functions into modules called muscle synergies. Current techniques use unsupervised dimensionality reduction algorithms to identify these synergies. However, these techniques rely on arbitrary criteria to determine the number of synergies, which is actually unknown. An example of such criteria is that the identified synergies must be able to reconstruct the measured muscle activity to at least a 90% level of accuracy. Thus, the residual muscle activity, the remaining 10% of the muscle activity, is often disregarded as noise. We show that residual muscle activity following muscle synergy identification has a large systematic effect on movements even when the number of synergies approaches the number of muscles. This suggests that current synergy extraction techniques may discard a component of muscle activity that is important for motor control. Therefore, current synergy extraction techniques must be updated to identify true physiological synergies.

scholarly journals Low-Dimensional Motor Control Representations in Throwing Motions

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Ana Lucia Cruz Ruiz ◽  
Charles Pontonnier ◽  
Georges Dumont
Keyword(s):  
Degrees Of Freedom ◽  
Muscle Activation ◽  
Extraction Methods ◽  
Muscle Synergies ◽  
Control Mechanisms ◽  
Dimensional Representation ◽  
Physical Conditions ◽  
Activation Level ◽  
Task Features ◽  
Low Dimensional

In this study, we identified a low-dimensional representation of control mechanisms in throwing motions from a variety of subjects and target distances. The control representation was identified at the kinematic level in task and joint spaces, respectively, and at the muscle activation level using the theory of muscle synergies. Representative features of throwing motions in all of these spaces were chosen to be investigated. Features were extracted using factorization and clustering techniques from the muscle data of unexperienced subjects (with different morphologies and physical conditions) during a series of throwing tasks. Two synergy extraction methods were tested to assess their consistency. For the task features, the degrees of freedom (DoF), and the muscles under study, the results can be summarized as (1) a control representation across subjects consisting of only two synergies at the activation level and of representative features in the task and joint spaces, (2) a reduction of control redundancy (since the number of synergies are less than the number of actions to be controlled), (3) links between the synergies triggering intensity and the throwing distance, and finally (4) consistency of the extraction methods. Such results are useful to better represent mechanisms hidden behind such dynamical motions and could offer a promising control representation for synthesizing motions with muscle-driven characters.

The Effect of Dynamic Posterior Stabilization on Facet Joint Contact Forces

Spine ◽  
2008 ◽  
Vol 33 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Christina A. Niosi ◽  
Derek C. Wilson ◽  
Qingan Zhu ◽  
Ory Keynan ◽  
David R. Wilson ◽  
...  
Keyword(s):  
Facet Joint ◽  
Contact Forces ◽  
Posterior Stabilization ◽  
Joint Contact
Sign in / Sign up

Export Citation Format

Share Document