The number and choice of muscles impact the results of muscle synergy analyses

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.

Modular control of gait after incomplete spinal cord injury: differences between sides

“…It is possible to find or identify additional synergies if the repertoire of analyzed muscles is larger 20 or the chosen muscles are different. 37 Nevertheless, considering that we measured both sides, and included the markers for 3D gait analysis, we consider that we reached a good compromise between measurement completeness and minimal experimental burden, with the latter being a critical factor when analyzing SCI patients. 16,18,23 The motor performance after a spinal cord injury evolves from the acute phase to the chronic period, and rehabilitation programs take advantage of the residual function both above and below the injury level.…”

Effects of height and load weight on shoulder muscle work during overhead lifting task

“…An OpenSim generic model of the shoulder based on three previous models (Holzbaur, Murray, and Delp 2005, Nikooyan et al 2011, Steele, Tresch, and Perreault 2013 was implemented. To that aim, the graphical user interface of OpenSim (Delp et al 2007) was used to add missing segments, joints and muscles.…”

“…Indeed, current musculoskeletal models present limits such as generic shoulder rhythm, lack of some shoulder muscles such as the trapezius and the serratus anterior (Holzbaur, Murray, and Delp 2005) and restriction at the glenohumeral joint (i.e. no degree of freedom (dof) at the acromioclavicular and sterno-clavicular joints) (Steele, Tresch, and Perreault 2013). The implementation of a new model from existing models would be necessary to assess muscle demand during lifting tasks.…”

Fault tolerant approach for biosignal-based robot control

“…Since muscles are synergetically activated to generate target movements as pointed out in previous studies, [29][30][31][32] the correlation among muscle activities can be a useful feature to classify sensor fault situations. The correlation of the ith and jth EMG channels is given as:…”