Control of Fast-Reaching Movements by Muscle Synergy Combinations

Abstract

How the CNS selects the appropriate muscle patterns to achieve a behavioral goal is an open question. To gain insight into this process, we characterized the spatiotemporal organization of the muscle patterns for fast-reaching movements. We recorded electromyographic activity from up to 19 shoulder and arm muscles during point-to-point movements between a central location and 8 peripheral targets in each of 2 vertical planes. We used an optimization algorithm to identify a set of time-varying muscle synergies, i.e., the coordinated activations of groups of muscles with specific time-varying profiles. For each one of nine subjects, we extracted four or five synergies whose combinations, after scaling in amplitude and shifting in time each synergy independently for each movement condition, explained 73-82% of the data variation. We then tested whether these synergies could reconstruct the muscle patterns for point-to-point movements with different loads or forearm postures and for reversal and via-point movements. We found that reconstruction accuracy remained high, indicating generalization across these conditions. Finally, the synergy amplitude coefficients were directionally tuned according to a cosine function with a preferred direction that showed a smaller variability with changes of load, posture, and endpoint than the preferred direction of individual muscles. Thus the complex spatiotemporal characteristics of the muscles patterns for reaching were captured by the combinations of a small number of components, suggesting that the mechanisms involved in the generation of the muscle patterns exploit this low dimensionality to simplify control.

Identification of synergies by optimization of trajectory tracking tasks

“…input space) with a linear vector space defined by the synergies (that serve as generators). The quality of the identified synergies is typically measured in the input space as the error in approximating a dataset of EMG data [1], [10], [4]. In this paper we took a different approach by proposing a (PCA-like) interpretation of synergies that minimizes approximation-errors directly in the task space (e.g.…”

“…activations of muscles with specific time-varying profiles [1]. There is evidence that weighted linear combinations of these elementary controls can account for a variety of tasks [4], [10].…”

“…We propose a method to identify the synergies that can optimally drive a given mechanical system along a set trajectories. From the theoretical point of view, this work contributes a novel interpretation of muscle synergies that is inspired by the Karhunen-Loève decomposition [14], and that capitalizes in assessing the quality of these control actions in task space (in terms of tracking error) rather then in inputs space (as it is typically done in neuroscience [1], [4]). …”

Unvealing the Principal Modes of Human Upper Limb Movements through Functional Analysis

“…In Butler et al (2010), the authors developed a quantitative method to assess upper limb motor deficits in children with cerebral palsy using three-dimensional motion analysis during the reach and grasp cycle. Other papers studied muscular synergies in upper limb activities, as in d ' Avella and Tresch (2002), where the authors introduced a model based on combinations of muscle time-varying synergies, and in d ' Avella et al (2006), where authors recorded electromyographic activity from shoulder and arm muscles during point-to-point movements. As for hand synergies, whose robotic applications are reviewed in Santello et al (2016), synergies have also been applied to movement generation for virtual arms (Fu et al, 2013) as well as myocontrol of a multi-DoF planar robotic arm using muscle synergies (Lunardini et al, 2015).…”

Synergy temporal sequences and topography in the spinal cord: evidence for a traveling wave in frog locomotion

“…With respect to activations of specific groups of muscles, synergies have been proposed as building blocks of motor control (Grillner 1981;Ting and Macpherson 2004;Cheung et al 2005Cheung et al , 2009d'Avella et al 2006;Krouchev et al 2006;Yakovenko et al 2011;Overduin et al 2012;Berger et al 2013;Bizzi and Cheung 2013;Krouchev and Drew 2013). There is also evidence that encoding of muscle synergies already takes place in the spinal cord (Saltiel et al 2001;Stein 2008;Hart and Giszter 2010;Roh et al 2011).…”