Effort minimization and synergistic muscle recruitment for three-dimensional force generation

Abstract

To generate a force at the hand in a given spatial direction and with a given magnitude the central nervous system (CNS) has to coordinate the recruitment of many muscles. Because of the redundancy in the musculoskeletal system, the CNS can choose one of infinitely many possible muscle activation patterns which generate the same force. What strategies and constraints underlie such selection is an open issue. The CNS might optimize a performance criterion, such as accuracy or effort. Moreover, the CNS might simplify the solution by constraining it to be a combination of a few muscle synergies, coordinated recruitment of groups of muscles. We tested whether the CNS generates forces by minimum effort recruitment of either individual muscles or muscle synergies. We compared the activation of arm muscles observed during the generation of isometric forces at the hand across multiple three-dimensional force targets with the activation predicted by either minimizing the sum of squared muscle activations or the sum of squared synergy activations. Muscle synergies were identified from the recorded muscle pattern using non-negative matrix factorization. To perform both optimizations we assumed a linear relationship between rectified and filtered electromyographic (EMG) signal which we estimated using multiple linear regressions. We found that the minimum effort recruitment of synergies predicted the observed muscle patterns better than the minimum effort recruitment of individual muscles. However, both predictions had errors much larger than the reconstruction error obtained by the synergies, suggesting that the CNS generates three-dimensional forces by sub-optimal recruitment of muscle synergies.

Muscle Synergy Constraints Do Not Improve Estimates of Muscle Activity From Static Optimization During Gait for Unimpaired Children or Children With Cerebral Palsy

“…Our results using synergies to improve estimations of muscle activation during gait contrasts with previous studies (Borzelli et al, 2013;Walter et al, 2014;Meyer et al, 2016;Serrancolí et al, 2016) which found generally good estimation of EMG with synergies. The differences between the previous work and our results here broadly fit into three categories: the optimization criteria, the challenge of relating EMG amplitudes to neural excitations, and generic musculoskeletal properties.…”

“…In the prior studies of Walter et al (2014), Meyer et al (2016), and Serrancolí et al (2016), EMG shape tracking was used as part of the optimization algorithm. In this study, we sought to model muscle activations through a modified SO cost function which minimized synergy activations squared, consistent with the optimization previously implemented by McKay and Ting (2012) and Borzelli et al (2013). This cost was motivated by the traditional physiologically motivated cost functions which seek to minimize fatigue or load in individual muscles (Crowninshield and Brand, 1981;Anderson and Pandy, 2001;Ackermann and Van Den Bogert, 2010), while constraining the space of allowable muscle activations to specified patterns FIGURE 6 | Increased sum of muscle stresses for SynSO: Muscle stress measured as the muscle activations squared increased for both TD and CP across all number of synergies.…”

“…One study applied synergy controls without EMG tracking and found better calculation of joint loads than individual EMG alone; however, this model was also highly calibrated for a single individual (Walter et al, 2014). Another study in the upper limb used optimization of synergy activations to model muscle activations during three-dimensional force generation and found that synergies better represented EMG data than independent muscle optimization (Borzelli et al, 2013).…”

Task constraints and minimization of muscle effort result in a small number of muscle synergies during gait

“…Based on this result, Borzelli et al (2013) suggest that the central nervous system generates forces at the hand by recruitment of muscle synergies rather than by recruitment of individual muscles. Their synergy-based calculation, however, is based on synergies extracted from measured EMG and uses the EMG-based muscle weightings as an input while calculating the activation patterns.…”

Test system of myoelectric signals to measure voltages to hand prosthesis

“…Hardyck and his colleagues were the first practitioners to apply the EMG. 3 In the early 80's Cram and Steger introduced a clinical method for scanning a variety of muscles using sensitive EMG device. 4 It was not until the middle of the 80's, when electrode techniques were integrated, which had a technological level sufficient for the production of small and low weight instrumentation.…”