Abstract

Finding muscle activity generating a given motion is a redundant problem, since there are many more muscles than degrees of freedom. The control strategies determining muscle recruitment from a redundant set are still poorly understood. One theory of motor control suggests that motion is produced through activating a small number of muscle synergies, i.e., muscle groups that are activated in a fixed ratio by a single input signal. Because of the reduced number of input signals, synergy-based control is low dimensional. But a major criticism on the theory of synergy-based control of muscles is that muscle synergies might reflect task constraints rather than a neural control strategy. Another theory of motor control suggests that muscles are recruited by optimizing performance. Optimization of performance has been widely used to calculate muscle recruitment underlying a given motion while assuming independent recruitment of muscles. If synergies indeed determine muscle recruitment underlying a given motion, optimization approaches that do not model synergy-based control could result in muscle activations that do not show the synergistic muscle action observed through electromyography (EMG). If, however, synergistic muscle action results from performance optimization and task constraints (joint kinematics and external forces), such optimization approaches are expected to result in low-dimensional synergistic muscle activations that are similar to EMG-based synergies. We calculated muscle recruitment underlying experimentally measured gait patterns by optimizing performance assuming independent recruitment of muscles. We found that the muscle activations calculated without any reference to synergies can be accurately explained by on average four synergies. These synergies are similar to EMG-based synergies. We therefore conclude that task constraints and performance optimization explain synergistic muscle recruitment from a redundant set of muscles.

Highlights

  • Walking is generated through the coordinated action of many muscles

  • We found that the muscle activations calculated without any reference to synergies can be accurately reconstructed by the combination of a small number of muscle synergies that are similar to EMG-based synergies

  • We investigated whether the low-dimensionality of muscle activity that has been observed through the analysis of EMG recorded during walking, can be explained by the combination of task constraints and the minimization of muscle effort

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Summary

Introduction

The number of muscles largely exceeds the number of degrees of freedom and the musculoskeletal system is highly redundant Because of this redundancy, a given walking pattern, characterized by joint kinematics and reaction forces between the ground and the feet, can be generated by infinitely many possible muscle recruitment strategies. The hypothesis of synergy-based or modular control has mainly been studied through analyzing electromyographic (EMG) activity of a subset of muscles measured during a variety of tasks Computational methods such as non-negative matrix factorization (NNMF), factor analysis, or independent component analysis are used to identify a set of synergies (for a comparison of methods see Ivanenko et al, 2005; Tresch et al, 2006). D’Avella and Pai (2010) proposed a new approach to assess the hypothesis of synergy-based modular control based on the adaptation rate to perturbations that are either compatible or incompatible with a modular control architecture. Berger et al (2013) found that adaptations to compatible perturbations were

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