Healthy subjects performed bilateral fast shoulder movements in different directions while standing on a force platform. Anticipatory postural adjustments were seen as changes in the electrical activity of postural muscles as well as displacements of the center of pressure and center of gravity. Postural muscle pairs of agonist-antagonist commonly demonstrated triphasic patterns starting prior to the first electromyographic (EMG) burst in the prime-mover muscle. Proximal postural muscles demonstrated the largest anticipatory increase in the background activity during movements in one of the two opposite directions (forward or backwards). These changes progressively decreased when movements deviated from the preferred direction and frequently disappeared during movements in the opposite direction. The patterns in distal muscles varied across subjects and could demonstrate larger anticipatory changes during movements forward and backwards as compared to movements in intermediate directions. Bilateral addition of inertial loads to the wrists did not change the general anticipatory patterns, while making some of their features more pronounced. Anticipatory postural adjustments were followed by later changes in the activity of postural muscles, also reflected in the mechanical variables. Changes in leg joint angles revealed a "hip-ankle strategy" during shoulder flexions and an "ankle strategy" during shoulder extensions. The study demonstrates different behaviors of proximal and distal muscles during anticipatory postural adjustments in preparation for fast arm movements. We suggest that the proximal muscles produce a general pattern of postural adjustments, while distal muscles take care of fine adjustments that are more likely to vary across subjects.

No abstract

This study presents a novel method for detection of the onset time of muscle activity using surface electromyogram (EMG) signals. The method takes advantage of the nonlinear properties of the Teager-Kaiser energy (TKE) operator, which simultaneously considers the amplitude and instantaneous frequency of the surface EMG, and therefore increases the prospects of muscle activity detection. To detect the onset time of muscle activity, the surface EMG signal was first processed by the TKE operator to highlight motor unit activities of the muscle. Then a robust threshold-based algorithm was developed in the TKE domain to locate the onset of muscle activity. The validity of the proposed method was illustrated using various surface EMG simulations as well as experimental surface EMG recordings.