Measuring multiple neuromuscular activation using EMG - a generalizability analysis.

Analysis of electromyography (EMG) data has been shown to be valuable in biomedical and clinical research. However, most analysis tools do not consider the non-linearity of EMG data or the synergistic effects of multiple neuromuscular activities. The SYNERGOS algorithm was developed to assess a single index based on non-linear analysis of multiple neuromuscular activation (MNA) of different muscles. This index has shown promising results in Parkinsonian gait, but it was yet to be explored whether the SYNERGOS index is generalizable. In this study, we evaluated generalizability of the SYNERGOS index over the course of several trials and over separate days with different walking speeds. Ten healthy adults aged from 18 to 40 years walked on a treadmill on two different days, while EMG data was collected from the upper and lower right leg. SYNERGOS indices were obtained and a generalizability analysis was conducted. The algorithm detected changes in MNA in response to altering gait speed and depicted a high generalizability coefficient ( ρ^2 ${\hat \rho ^2}$ ) of 0.823 with a standard error of 5.117 with nominal inter-trial or inter-day effects. We concluded SYNERGOS may be a valuable tool in EMG analysis due to its generalizability and its sensitivity to task modifications and associated neuromotor changes.