Novel Methods to Enhance Precision and Reliability in Muscle Synergy Identification during Walking

Muscle synergies are hypothesized to reflect modular control of muscle groups via descending commands sent through multiple neural pathways. Recently, the number of synergies has been reported as a functionally relevant indicator of motor control complexity in individuals with neurological movement disorders. Yet the number of synergies extracted during a given activity, e.g., gait, varies within and across studies, even for unimpaired individuals. With no standardized methods for precise determination, this variability remains unexplained making comparisons across studies and cohorts difficult. Here, we utilize k-means clustering and intra-class and between-level correlation coefficients to precisely discriminate reliable from unreliable synergies. Electromyography (EMG) was recorded bilaterally from eight leg muscles during treadmill walking at self-selected speed. Muscle synergies were extracted from 20 consecutive gait cycles using non-negative matrix factorization. We demonstrate that the number of synergies is highly dependent on the threshold when using the variance accounted for by reconstructed EMG. Beyond use of threshold, our method utilized a quantitative metric to reliably identify four or five synergies underpinning walking in unimpaired adults and revealed synergies having poor reproducibility that should not be considered as true synergies. We show that robust and unreliable synergies emerge similarly, emphasizing the need for careful analysis in those with pathology.