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.

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.

Electromyography (EMG) is a commonly used technique to record myoelectric signals, i.e., motor neuron signals that originate from the central nervous system (CNS) and synergistically activate groups of muscles resulting in movement. EMG patterns underlying movement, recorded using surface or needle electrodes, can be used to detect movement and gait abnormalities. In this review article, we examine EMG signal processing techniques that have been applied for diagnosing gait disorders. These techniques span from traditional statistical tests to complex machine learning algorithms. We particularly emphasize those techniques are promising for clinical applications. This study is pertinent to both medical and engineering research communities and is potentially helpful in advancing diagnostics and designing rehabilitation devices.

The human motor system is a complex neuro-musculo sensory system that needs further investigations of neuro-muscular commands and sensory-motor coupling to decode movement execution. Some researchers suggest that the central nervous system (CNS) activates a small set of modules termed muscle synergies to simplify motor control. Further, these modules form functional building blocks of movement as they can explain the neurophysiological characteristics of movements. We can identify and extract these muscle synergies from electromyographic signals (EMG) recorded in the laboratory by using linear decomposition algorithms, such as principal component analysis (PCA) and non-Negative Matrix Factorization Algorithm (NNMF). For the past three decades, the hypothesis of muscle synergies has received considerable attention as we attempt to understand and apply the concept of muscle synergies in clinical settings and rehabilitation. In this article, we first explore the concept of muscle synergies. We then present different strategies of adaptation in these synergies that the CNS employs to accomplish a movement goal.

Summary: 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.

We present a framework for the detection of swing and stance phases of gait cycle during walk on a perturbing platform, i.e., a slackline. The framework employs a time varying Kalman filter to reconstruct neural commands from the accelerometer data acquired from slackline perturbations that are generated by the participants. Electromyography (EMG) signals reconstructed from the estimated neural command were compared with surface EMG recordings during slackline walking. For four gait trials, the EMG signals were reconstructed with 92%-95% accuracy. These reconstructed signals were dimensionally reduced using principal component analysis (PCA) and classified using support vector machine (SVM), Decision Tree (DT), Naive Bayes (NB) classifiers. The stance and swing phases were discriminated, the performance of each classifiers was evaluated and classification accuracy was later enhanced with post processing. The DT algorithm outperformed others in detecting the stance and swing phases of gait cycle on slackline with 99.5% accuracy.