Minimum Electromyography Sensor Set Needed to Identify Age-Related Impairments in the Neuromuscular Control of Walking Using the Dynamic Motor Control Index

Abstract

The dynamic motor control index is an emerging biomarker of age-related neuromuscular impairment. To date, it has been computed by quantifying the co-activity of eleven lower limb muscles. Because clinics that routinely employ electromyography typically collect from fewer muscles, a reduced muscle sensor set may improve the clinical usability of this metric of motor control. This study aimed to test if commonly used eight- and five-muscle electromyography (EMG) sensor sets produce similar dynamic motor control indices as the previously examined eleven-muscle sensor set and similarly differentiate across age subgroups. EMG data were collected during treadmill walking from 36 adults separated into young (N = 18, <35 yrs.), young-old (N = 13, 65-74 yrs.), and old-old (N = 5, ≥75 yrs.) subgroups. Dynamic motor control indices generated using the sensor set with eleven muscles correlated with the eight-muscle set (R² = 0.70) but not the five-muscle set (R² = 0.30). Regression models using the eleven-muscle (χ²(4) = 10.62, p = 0.031, Nagelkerke R² = 0.297) and eight-muscle (χ²(4) = 9.418, p = 0.051, Nagelkerke R² = 0.267) sets were significant and approaching significance, respectively, whereas the model for the five-muscle set was not significant (p = 0.663, Nagelkerke R² = 0.073). In both the eleven-muscle (Wald χ² = 5.16, p = 0.023, OR = 1.26) and eight-muscle models (Wald χ² = 4.20, p = 0.04, OR = 1.19), a higher index significantly predicted being in the young group compared to the old-old group. Age-related differences in the neuromuscular control of walking can be detected using dynamic motor control indices generated using eleven- and eight-muscle sensor sets, increasing clinical usability of the dynamic motor control index.

Keywords: aging; electromyography; gait; motor control; muscle activity; walking.

Figure 1

(A) EMG data were collected from 11 different muscles. Reduced muscle sets of 8 and 5 muscles were selected for continuity with prior research; (B) Exemplary EMG data from the 11 different muscles for one participant; (C) Dynamic motor control index and muscle synergy calculations for an exemplary participant. EMG data from 5, 8, or 11 muscles were inputted into a non-negative matrix factorization algorithm. This algorithm calculates the best solution for 1 synergy up to (#muscles—1) synergies and reports the solution’s variability accounted for (VAF). The 1-synergy solution is used to calculate the dynamic motor control index. The first solution with >90% of the VAF (or until a new synergy does not increase VAF by at least 5%) is the muscle synergy solution.

Figure 2

(A) Average ± standard error dynamic motor control indices and (B) number of muscle synergies computed for the 11, 8, and 5 muscle sensor sets.

Figure 3

Dynamic motor control indices of the 8-muscle set are highly correlated with the 11-muscle set (R² = 0.70), while the 5-muscle set is not (R² = 0.30).