Abstract
One theory for how humans control movement is that muscles are activated in weighted groups or synergies. Studies have shown that electromyography (EMG) from a variety of tasks can be described by a low-dimensional space thought to reflect synergies. These studies use algorithms, such as nonnegative matrix factorization, to identify synergies from EMG. Due to experimental constraints, EMG can rarely be taken from all muscles involved in a task. However, it is unclear if the choice of muscles included in the analysis impacts estimated synergies. The aim of our study was to evaluate the impact of the number and choice of muscles on synergy analyses. We used a musculoskeletal model to calculate muscle activations required to perform an isometric upper-extremity task. Synergies calculated from the activations from the musculoskeletal model were similar to a prior experimental study. To evaluate the impact of the number of muscles included in the analysis, we randomly selected subsets of between 5 and 29 muscles and compared the similarity of the synergies calculated from each subset to a master set of synergies calculated from all muscles. We determined that the structure of synergies is dependent upon the number and choice of muscles included in the analysis. When five muscles were included in the analysis, the similarity of the synergies to the master set was only 0.57 ± 0.54; however, the similarity improved to over 0.8 with more than ten muscles. We identified two methods, selecting dominant muscles from the master set or selecting muscles with the largest maximum isometric force, which significantly improved similarity to the master set and can help guide future experimental design. Analyses that included a small subset of muscles also over-estimated the variance accounted for (VAF) by the synergies compared to an analysis with all muscles. Thus, researchers should use caution using VAF to evaluate synergies when EMG is measured from a small subset of muscles.
Highlights
The human musculoskeletal system is complex, providing a robust and flexible system for executing tasks of daily life
IMPACT OF THE CHOICE OF MUSCLES ON SYNERGIES To improve the similarity of synergies estimated from a subset of muscles to the master set, we evaluated different methods for choosing which muscles to include in the analysis
COMPARISON OF EXPERIMENTAL AND MODEL SYNERGIES Synergies estimated from a musculoskeletal model of an upperextremity isometric force task were similar to synergies calculated from experimental EMG (Figure 2)
Summary
The human musculoskeletal system is complex, providing a robust and flexible system for executing tasks of daily life. Previous studies have shown that muscle activity during a variety of tasks in humans (Ting and Macpherson, 2005; Ivanenko et al, 2007; Cheung et al, 2012) and animals (Tresch et al, 2002; d’Avella and Bizzi, 2005) can be described by a low-dimensional space thought to reflect synergies. In these experiments, electromyography (EMG) is measured during a variety of tasks and matrix factorization algorithms, such as nonnegative matrix factorization (NNMF), are used to determine a subset of vectors, or synergies, which describe the EMG signals. Gait, and upper-extremity tasks in humans, a lower dimensional space of four to six synergies have consistently been shown to describe muscle activity (Ivanenko et al, 2006; Torres-Oviedo et al, 2006; Roh et al, 2012)
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