The central nervous system (CNS) is believed to utilize specific predefined modules, called muscle synergies (MS), to accomplish a motor task. Yet questions persist about how the CNS combines these primitives in different ways to suit the task conditions. The MS hypothesis has been a subject of debate as to whether they originate from neural origins or nonneural constraints. In this review article, we present three aspects related to the MS hypothesis: (1) the experimental and computational evidence in support of the existence of MS, (2) algorithmic approaches for extracting them from surface electromyography (EMG) signals, and (3) the possible role of MS as a neurorehabilitation tool. We note that recent advances in computational neuroscience have utilized the MS hypothesis in motor control and learning. Prospective advances in clinical, medical, and engineering sciences and in fields such as robotics and rehabilitation stand to benefit from a more thorough understanding of MS.

We present a novel formulation that employs task-specific muscle synergies and state-space representation of neural signals to tackle the challenging myoelectric control problem for lower arm prostheses. The proposed framework incorporates information about muscle configurations, e.g., muscles acting synergistically or in agonist/antagonist pairs, using the hypothesis of muscle synergies. The synergy activation coefficients are modeled as the latent system state and are estimated using a constrained Kalman filter. These task-dependent synergy activation coefficients are estimated in real-time from the electromyogram (EMG) data and are used to discriminate between various tasks. The task discrimination is helped by a post-processing algorithm that uses posterior probabilities. The proposed algorithm is robust as well as computationally efficient, yielding a decision with > 90% discrimination accuracy in approximately 3 ms . The real-time performance and controllability of the algorithm were evaluated using the targeted achievement control (TAC) test. The proposed algorithm outperformed common machine learning algorithms for single- as well as multi-degree-of-freedom (DOF) tasks in both off-line discrimination accuracy and real-time controllability (p < 0.01).

Active lower limb transfemoral prostheses have enabled amputees to perform different locomotion modes such as walking, stair ascent, stair descent, ramp ascent and ramp descent. To achieve seamless mode transitions, these devices either rely on neural information from the amputee's residual limbs or sensors attached to the prosthesis to identify the intended locomotion modes or both. We present an approach for classification of locomotion modes based on the framework of muscle synergies underlying electromyography signals. Neural information at the critical instances (e.g., heel contact and toe-off) was decoded for this purpose. Non-negative matrix factorization was used to extract the muscles synergies from the muscle feature matrix. The estimation of the neural command was done using non-negative least squares. The muscle synergy approach was compared with linear discriminant analysis (LDA), support vector machine (SVM), and neural network (NN) and was tested on seven able-bodied subjects. There was no significant difference ( p > 0.05 ) in transitional and steady state classification errors during stance phase. The muscle synergy approach performed significantly better ( ) than NN and LDA during swing phase while results were similar to SVM. These results suggest that the muscle synergy approach can be used to discriminate between locomotion modes involving transitions.

It is hypothesised that specific groups of muscles aka muscle synergies (MSs) are combined by the central nervous system to control a wide repertoire of movements and also simplify motor control. Therefore, studying MSs during human locomotion is of significance, as it may reveal neuromuscular strategies for postural stability. In this study, the authors aimed to use the hypothesis of MSs to identify specific muscle co-activations during overground walking and slacklining where postural perturbations were generated by the participants rather than being externally controlled. Nine participants were asked to walk overground and on a slackline while they recorded myoelectric activity of their leg muscles. They extracted synergies from the electromyography signals in the two tasks using factor analysis. The results showed adaptation in the shared MSs structure during walking on the slackline and these shared MSs across participants were recruited flexibly to meet the demand for stability. The modulation of synergies suggests adaptive neuromuscular strategies for stability while walking on a slackline. Specifically, higher activation of quadriceps during slacklining suggests a crouched gait to facilitate balance. During overground walking, lower leg muscles revealed higher activation compared to slacklining to support a more consistent toe-off during the stance phase.

In neurophysiology, a hypothesis under investigation relates to how neural modularity helps in learning of skills. Accordingly, we studied differences in muscle synergy (MS) organization at three different proficiency levels on a task more challenging than walking. Our study included slackline walking whereby the perturbations to evoke postural responses are generated by the participants rather than externally controlled. Furthermore, studying MS of individuals with different proficiency levels under such constraints will provide us an understanding of different strategies for dynamic postural stability. Hence, the main aim of our study is to identify MS associated with proficiency during slacklining. Muscle Synergies and their activation coefficients were extracted using factor analysis on electromyography that was recorded from lower limb muscles. The spatial and temporal profiles were analyzed to examine muscle co-activation patterns for stability across three different groups of slackliners (high, moderate, and nonproficient). We found three robust MS structures across all skill levels associated with crouched gait while slacklining. Higher activation of quadriceps, gastrocnemius, and hamstrings with tibialis anterior was observed for synergy one, two, and three, respectively. An additional proficiency-based synergy was recruited for highly proficient slackliners, and similarly for nonproficient ones. For highly proficient slackliners, the additional synergy was in relation to lowering of the center of mass for consistent stabilization. For nonproficient slackliners (PS), the recruitment of additional synergy was related to consistent knee flexion with the higher range of motion. Overall, our work showed alteration in the modular organization of MS at different proficiency levels that could be associated with differences in knee kinematics during slacklining. We think that the outcomes of our study regarding differences in the MS organization based on proficiency levels, and the underlying neuro-physiological features, will facilitate rehabilitation of individuals with balance disorders.