Does thenumber of stepsneededfor UCM gait analysisdiffers between healthy and stroke?

The basis for the uncontrolled manifold (UCM) approach is the variability among repetitions of a motor task. Thus, reliable results might be influenced by the number of trials. This study estimated the number of steps needed for UCM analysis of stroke gait and if it is the same for healthy subjects. Twenty-five volunteers participated, sixteen in the stroke group (age 59.0 ± 7.5 years, ten hemiparesis at right), and nine in the healthy group (age 59.2 ± 4.9 years). We applied the UCM analysis over each lower limb's single support phase (SSP). The center of mass in the sagittal plane was the task variable, and the ankle, knee and hip joint angles, the elemental variables. The results obtained with 40 steps were used as a reference and compared with those obtained separately from 10, 20, and 30 steps. The mean values of the curves along the SSP were compared between the sets of steps. Further, for each volunteer, we calculated the Pearson correlation between the 40 steps curve and those obtained with other numbers of steps. Our results indicate that (1) the number of steps necessary to perform UCM analysis of stroke gait is larger than those necessary in healthy condition, (2) the synergy index is less sensitive to the number of steps than the UCM components (V_UCM and V_ORT), and (3) the analysis of the UCM over time requires a more significant number of steps than the mean values.

Influence of sensor mass and adipose tissue on the mechanomyography signal of elbow flexor muscles.

Mechanomyography (MMG) is a non-invasive technique that records muscle contraction using sensors positioned on the skin's surface. Therefore, it can have its signal attenuated due to the adipose tissue, directly influencing the results. This study evaluates the influence of different mass added to a sensor's assembly and the adipose tissue on MMG signals of elbow flexor muscles. Test protocol consisted of skinfold thickness measurement of 22 volunteers, followed by applying 2-3 s electrical stimulation for muscle contraction during the acquisition of MMG signals. MMG signals were processed in the time domain, using the average of the absolute amplitude, and expressed in gravity values (G), termed here as MMG(G). Tests occurred four times with different sensor masses. MMG data were processed and analyzed statistically using Friedman and Kruskal-Wallis tests to determine the differences between the MMG signals measured with different sensor masses. The Mann-Whitney analysis indicated differences in the MMG signals between groups with different skinfold thickness. MMG(G) signals suffered attenuation with increasing sensor mass (0.4416 G to 0.94 g; 0.3902 G to 2.64 g; 0.3762 G to 5.44 g; 0.3762 G to 7.14 g) and adipose tissue.