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Identification of synergies by optimization of trajectory tracking tasks

According to the model of muscle synergies, the central nervous system (CNS) is organised in a modular structure, such that any muscle activation can be produced as a linear superposition of predefined time-varying profiles (i.e. synergies). This organisation might contribute to simplify the control of the musculoskeletal apparatus. Taking inspiration from these findings, we propose a method to identify the synergies that can be used to control a given dynamical system for the task of tracking a set of trajectories. Further, we show how the same approach can be applied to assess the impact of the number of synergies on the performance of the control method. From the theoretical point of view, we provide a novel interpretation of synergies inspired by the Karhunen-Loève decomposition; furthermore, our method suggests that the quality of a set of synergies should be measured in task space rather then in input space.