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.

Keywords: dimensionality reduction; modularity; motor control; muscle synergies; review; robotics; task-space.

Figure 1

Comparative scheme between research on muscle synergies in neuroscience and control engineering.

Figure 2

Different models of muscle synergies. The temporal and the synchronous models explain motor signals as linear combinations of muscle balance vectors (spatial patterns), with 1-dimensional time-varying coefficients (A). In the temporal model, these coefficients serve as task-independent predefined modules, and the spatial patterns as the new (task-dependent) control input. In the synchronous model, on the other hand, the control input is represented by the temporal patterns, while the spatial patterns act as predefined modules. Finally, time-varying synergies are spatio-temporal predefined motor patterns, which can be scaled in amplitude and shifted in time by the new input coefficients (B).

Figure 3

Procedures for the identification and the testing of muscle synergies. In experimental neuroscience (green arrows), initially a group of subjects perform the tasks prescribed by the experimenter (A). The EMG signals acquired during the experiments (B) are then analyzed, and a dimensionality reduction algorithm is applied to obtain the synergies (C). Very often such synergies are not evaluated at the task-level (dashed arrow), therefore there is no guarantee that they lead to the observed task performance. In robotics (red arrows), synergies are synthesized (C) based on the requirements of the desired class of tasks (A). Then they are appropriately combined to generate the motor signals (B) to solve a specific task instance. The quality of the synthesized synergies is finally tested in terms of the obtained task performance (A). Without loss of generality, the figure presents the time-varying synergy model; however, the previous description holds for all the models.