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

Barradas, Victor R.; Kutch, Jason J.; Kawase, Toshihiro; Koike, Yasuharu; Schweighofer, Nicolas

doi:10.1152/jn.00472.2019

Cited by 28 publications

(40 citation statements)

References 47 publications

(53 reference statements)

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“…We found that a number of modules ranging from 3 to 5 reconstructed the original EMG for all subjects in each of the directions. While recent studies suggest that it is arbitrary to neglect a part of the reconstruction R 2 , which might be meaningful for movement generation (Barradas et al, 2019), these results are in good accordance with previous studies based on similar reconstruction criteria, which already demonstrated that, in a limited subset of movements of the upper-limb, a 17-muscle model can be reduced to the coordination of five motor modules with time-varying synergies (d’Avella et al, 2006). In the present experiment, starting from a comparable number of EMG channels, a limited number of modules, ranging from 3 to 5, was found for each of the considered mappings.…”

Section: Discussionsupporting

confidence: 89%

A Comprehensive Spatial Mapping of Muscle Synergies in Highly Variable Upper-Limb Movements of Healthy Subjects

et al. 2019

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BackgroundRecently, muscle synergy analysis has become a standard methodology for extracting coordination patterns from electromyographic (EMG) signals, and for the evaluation of motor control strategies in many contexts. Most previous studies have characterized upper-limb muscle synergies across a limited set of reaching movements. With the aim of future uses in motor control, rehabilitation and other fields, this study provides a comprehensive characterization of muscle synergies in a large set of upper-limb tasks and also considers inter-individual and environmental variability.MethodsSixteen healthy subjects performed upper-limb hand exploration movements for a comprehensive mapping of the upper-limb workspace, which was divided into several sectors (Frontal, Right, Left, Horizontal, and Up). EMGs from representative upper-limb muscles and kinematics were recorded to extract muscle synergies and explore the composition, repeatability and similarity of spatial synergies across subjects and movement directions, in a context of high variability of motion.ResultsEven in a context of high variability, a reduced set of muscle synergies may reconstruct the original EMG envelopes. Composition, repeatability and similarity of synergies were found to be shared across subjects and sectors, even if at a lower extent than previously reported.ConclusionExtending the results of previous studies, which were performed on a smaller set of conditions, a limited number of muscle synergies underlie the execution of a large variety of upper-limb tasks. However, the considered spatial domain and the variability seem to influence the number and composition of muscle synergies. Such detailed characterization of the modular organization of the muscle patterns for upper-limb control in a large variety of tasks may provide a useful reference for studies on motor control, rehabilitation, industrial applications, and sports.

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Section: Discussionsupporting

confidence: 89%

A Comprehensive Spatial Mapping of Muscle Synergies in Highly Variable Upper-Limb Movements of Healthy Subjects

et al. 2019

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“…Aiming error doesn’t decline to less than 10% until the number of synergies equals the number of muscles (Aymar de Rugy et al, 2013 ). A similar conclusion was reached for a virtual force task involving 10 shoulder and elbow muscles moving a splinted forearm ( Barradas, Kutch, Kawase, Koike, & Schweighofer, 2020 ).…”

Section: How Might Humans Learn To Use Muscles?supporting

confidence: 65%

Learning to Use Muscles

Loeb

2021

Journal of Human Kinetics

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The human musculoskeletal system is highly complex mechanically. Its neural control must deal successfully with this complexity to perform the diverse, efficient, robust and usually graceful behaviors of which humans are capable. Most of those behaviors might be performed by many different subsets of its myriad possible states, so how does the nervous system decide which subset to use? One solution that has received much attention over the past 50 years would be for the nervous system to be fundamentally limited in the patterns of muscle activation that it can access, a concept known as muscle synergies or movement primitives. Another solution, based on engineering control methodology, is for the nervous system to compute the single optimal pattern of muscle activation for each task according to a cost function. This review points out why neither appears to be the solution used by humans. There is a third solution that is based on trial-and-error learning, recall and interpolation of sensorimotor programs that are good-enough rather than limited or optimal. The solution set acquired by an individual during the protracted development of motor skills starting in infancy forms the basis of motor habits, which are inherently low-dimensional. Such habits give rise to muscle usage patterns that are consistent with synergies but do not reflect fundamental limitations of the nervous system and can be shaped by training or disability. This habit-based strategy provides a robust substrate for the control of new musculoskeletal structures during evolution as well as for efficient learning, athletic training and rehabilitation therapy.

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“…However, at the same time, the quality of the EMG signals reconstruction was slightly degraded. This finding demonstrated that a trade-off between the capability of the extracted muscle synergies to better describe the EMG signals variability and the task performance in terms of force reconstruction might exist and can be exploited to develop more intuitive myo-controllers that are mainly evaluated in the task space [19,41].…”

Section: Discussionmentioning

confidence: 99%

“…Few works have investigated the concept of functional synergies that are an initial attempt to link muscle synergies with task variables [19,[38][39][40]. However, as deeply discussed by Barradas et al [41] and Cristiano et al [19], functional synergies present some issues and limitations. After an extensive argumentation, Cristiano and his colleagues state that a novel required technique for muscle synergy extraction ".…”

Section: Introductionmentioning

confidence: 99%

Task-Oriented Muscle Synergy Extraction Using An Autoencoder-Based Neural Model

et al. 2020

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The growing interest in wearable robots opens the challenge for developing intuitive and natural control strategies. Among several human–machine interaction approaches, myoelectric control consists of decoding the motor intention from muscular activity (or EMG signals) with the aim of driving prosthetic or assistive robotic devices accordingly, thus establishing an intimate human–machine connection. In this scenario, bio-inspired approaches, e.g., synergy-based controllers, are revealed to be the most robust. However, synergy-based myo-controllers already proposed in the literature consider muscle patterns that are computed considering only the total variance reconstruction rate of the EMG signals, without taking into account the performance of the controller in the task (or application) space. In this work, extending a previous study, the authors presented an autoencoder-based neural model able to extract muscles synergies for motion intention detection while optimizing the task performance in terms of force/moment reconstruction. The proposed neural topology has been validated with EMG signals acquired from the main upper limb muscles during planar isometric reaching tasks performed in a virtual environment while wearing an exoskeleton. The presented model has been compared with the non-negative matrix factorization algorithm (i.e., the most used approach in the literature) in terms of muscle synergy extraction quality, and with three techniques already presented in the literature in terms of goodness of shoulder and elbow predicted moments. The results of the experimental comparisons have showed that the proposed model outperforms the state-of-art synergy-based joint moment estimators at the expense of the quality of the EMG signals reconstruction. These findings demonstrate that a trade-off, between the capability of the extracted muscle synergies to better describe the EMG signals variability and the task performance in terms of force reconstruction, can be achieved. The results of this study might open new horizons on synergies extraction methodologies, optimized synergy-based myo-controllers and, perhaps, reveals useful hints about their origin.

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

Cited by 28 publications

References 47 publications

A Comprehensive Spatial Mapping of Muscle Synergies in Highly Variable Upper-Limb Movements of Healthy Subjects

A Comprehensive Spatial Mapping of Muscle Synergies in Highly Variable Upper-Limb Movements of Healthy Subjects

Learning to Use Muscles

Task-Oriented Muscle Synergy Extraction Using An Autoencoder-Based Neural Model

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