Muscle Synergy Assessment During Single-Leg Stance

Abstract

In the study of muscle synergies during the maintenance of single-leg stance there are several methodological issues that must be taken into account before muscle synergy extraction. In particular, it is important to distinguish between epochs of surface electromyography (sEMG) signals corresponding to "well-balanced" and "unbalanced" single-leg stance, since different motor control strategies could be used to maintain balance. The aim of this work is to present and define a robust procedure to distinguish between "well-balanced" and "unbalanced" single-leg stance to be chosen as input for the algorithm used to extract muscle synergies. Our results demonstrate that the proposed approach for the selection of sEMG epochs relative to "well-balanced" and "unbalanced" single-leg stance is robust with respect to the selection of the segmentation threshold, revealing a high consistency in the number of muscle synergies and high similarity among the weight vectors (correlation values range from 0.75 to 0.97). Moreover, differences in terms of average recruitment levels and balance control strategies were detected, suggesting a slightly different modular organization between "well-balanced" and "unbalanced" single-leg stance. In conclusion, this approach can be successfully used as a pre-processing step before muscle synergy extraction, allowing for a better assessment of motor control strategies during the singleleg stance task.

An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

“…First, gait cycles were segmented and time-normalized to 1000 samples. Second, signals were high-pass filtered at 35 Hz through an 8th order Butterworth digital filter to attenuate slow movement artifacts and baseline wandering [ 22 , 35 , 36 ]. Third, signals were demeaned and rectified.…”

“…Third, signals were demeaned and rectified. Fourth, rectified signals were low-pass filtered at 12 Hz through a 5th order Butterworth digital filter to obtain the sEMG envelopes [ 22 , 35 , 36 ]. Fifth, each envelope was normalized in amplitude with respect to its global maximum.…”

Muscle synergies for the control of single-limb stance with and without visual information in young individuals

“…Muscle synergy extraction and sorting procedures were performed in accordance with our previous study [ 34 ]. Briefly, the segmented EMG signals corresponding to single-limb stance tasks were high-pass filtered at a cut-off frequency of 35 Hz through an 8th order zero-lag IIR Butterworth digital filter, full-wave rectified, and low-pass filtered at a cut-off frequency of 12 Hz through a 5th order zero-lag IIR Butterworth digital filter [ 35 ].…”

“…The tVAF was used to select the optimal number of muscle synergies ( N opt ) needed to properly reconstruct the original EMG signals and to accurately assess the motor control strategies. As detailed in our previous work [ 34 ], the N opt was selected by consecutively applying a global criterion on each number of synergies from 1 to 8 (least number of synergies granting a tVAF ≥ 90%) and a local criterion on the number of muscle synergies selected through the global criterion ( VAF ≥ 75% for each of the observed muscles) [ 30 , 38 , 39 ].…”

“…The ankle control strategy ( S ankle ) was mainly identified by the activation of 5 leg muscles (PL, PB, LG, TA, and SO), the knee control strategy ( S knee ) by the activation of 3 shank muscles (VM, VL, and RF), and the hip/trunk control strategy ( ) by the activation of 5 muscles of the proximal lower limb and the trunk (BF, ST, GM, LDO, and LDC). The computation of the balance control strategies is described in detail in our previous study [ 34 ].…”