HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives.

Abstract : In this paper we review the works related to muscle synergies that have been carried-out in neuroscience and control engineering. In particular, we refer to the hypothesis that the central nervous system (CNS) generates desired muscle contractions by combining a small number of predefined modules, called muscle synergies. We provide an overview of the methods that have been employed to test the validity of this scheme, and we show how the concept of muscle synergy has been generalized for the control of artificial agents. The comparison between these two lines of research, in particular their different goals and approaches, is instrumental to explain the computational implications of the hypothesized modular organization. Moreover, it clarifies the importance of assessing the functional role of muscle synergies: although these basic modules are defined at the level of muscle activations (input-space), they should result in the effective accomplishment of the desired task. This requirement is not always explicitly considered in experimental neuroscience, as muscle synergies are often estimated solely by analyzing recorded muscle activities. We suggest that synergy extraction methods should explicitly take into account task execution variables, thus moving from a perspective purely based on input-space to one grounded on task-space as well.
Document type :
Journal articles
Complete list of metadata

Contributor : Lucille Gay Connect in order to contact the contributor
Submitted on : Thursday, August 28, 2014 - 12:04:24 PM
Last modification on : Thursday, April 28, 2022 - 5:09:35 PM

Links full text



Cristiano Alessandro, Ioannis Delis, Francesco Nori, Stefano Panzeri, Bastien Berret. Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives.. Frontiers in Computational Neuroscience, Frontiers, 2013, 7, pp.43. ⟨10.3389/fncom.2013.00043⟩. ⟨hal-01058831⟩



Record views