Trajectory Adjustments Underlying Task-Specific Intermittent Force Behaviors and Muscular Rhythms

Abstract

Force intermittency is one of the major causes of motor variability. Focusing on the dynamics of force intermittency, this study was undertaken to investigate how force trajectory is fine-tuned for static and dynamic force-tracking of a comparable physical load. Twenty-two healthy adults performed two unilateral resistance protocols (static force-tracking at 75% maximal effort and dynamic force-tracking in the range of 50%–100% maximal effort) using the left hand. The electromyographic activity and force profile of the designated hand were monitored. Gripping force was off-line decomposed into a primary movement spectrally identical to the target motion and a force intermittency profile containing numerous force pulses. The results showed that dynamic force-tracking exhibited greater intermittency amplitude and force pulse but a smaller amplitude ratio of primary movement to force intermittency than static force-tracking. Multi-scale entropy analysis revealed that force intermittency during dynamic force-tracking was more complex on a low time scale but more regular on a high time scale than that of static force-tracking. Together with task-dependent force intermittency properties, dynamic force-tracking exhibited a smaller 8–12 Hz muscular oscillation but a more potentiated muscular oscillation at 35–50 Hz than static force-tracking. In conclusion, force intermittency reflects differing trajectory controls for static and dynamic force-tracking. The target goal of dynamic tracking is achieved through trajectory adjustments that are more intricate and more frequent than those of static tracking, pertaining to differing organizations and functioning of muscular oscillations in the alpha and gamma bands.

Differences in Force Gradation between Tug-of-War Athletes and Non-Athletes during Rhythmic Force Tracking at High Exertion Levels

“…However, it is worth noting that young men in the previous study received only low-intensity strength training with free-weight exercises, and force fluctuations were assessed with static isometric contraction at relatively low force levels (6-60% MVC). The tug-of-war athletes in the present study were heavily trained and assessed with a force task with a complex nature because a dynamic force task usually exhibits a more vigorous force-grading challenge than a static force task (2,6). On the other hand, strength training in the tug-of war athletes did not contribute to greater force steadiness, as reported in aged adults following strengthening with light or heavy weights (16).…”

“…The conditioned force output was dichotomized into two different force components, force fluctuations and primary contraction ( Fig. 1) (2,18). The force fluctuation profile was obtained by conditioning the force output with a zero-phasing notch filter that passes all frequencies except for a target rate at 0.5 Hz.…”

“…F mean , mean force output; F mean /BM, ratio of mean force output to body mass; STD_ FF, standard deviation of force fluctuations; R FF/Fmean , ratio of the force fluctuations to mean force output; ApEn, approximate entropy; Pulse gain, amplitude-duration regression slope of force pulse. ***Non-athlete > Tug-of-war, P ≤ 0.001; † Tug-of-war > Non-athlete, P < 0.05; † † † Tug-of-war > Non-athlete, P < 0.001. differences between the tug-of-war athletes and nonathletes in the following aspects: [1] force characteristics (F mean , F mean /BM, and normalized tracking error); [2]) force fluctuation properties (STD_ FF , R FF/Fmean , and ApEn); [3]) spectral parameters of force fluctuations (mean frequency and spectral dispersion); and [4]) force pulse variables (pulse amplitude, pulse duration, and pulse gain). The levels of significance for the Hotelling's T 2 statistics and post-hoc comparisons were 0.05 using Bonferroni correction.…”

“…Task difficulty (2) and availability of visual information (12) are known to affect characteristics of low-frequency force fluctuations. Dynamic force-tracking that entails meticulous scaling of force outputs across various force levels exhibited larger force fluctuations with more complex on a low time scale than static force-tracking of a comparable exertion level (2). This observation revealed that dynamic force task is more demanding than static force task, involving a vast amount of intricate trajectory adjustments to satisfy the task needs.…”

“…Inherent with numerous force pulses (21), a force profile can never be completely smooth. Owing to intermittent use of afferent information by the brain to counter feedback delays (21,29), low-frequency force fluctuations below 3 Hz are functionally important to gain insight into force regulation of visually-guided force tasks (21,29), as they are linked to processing capacity of visual information (31) and error-correction strategies to remedy trajectory deviations (2,23). There are two important dimensions of force fluctuations: the size and the regularity.…”