Grip Force Complexity Under Constraint: Evidence from Fractal and Entropy Analyses

The present study investigates how grip force (GF) and load force (LF) dynamics reorganize under varying task constraints, focusing on the fractal and entropic properties of motor output. Twenty healthy adults performed precision grip tasks across five force conditions: two spontaneous conditions (pre, post) without visual feedback and three target-driven conditions (natural, -10%, +10% of natural). The temporal and informational structure of GF and LF signals were quantified using the Hurst exponent (H) and Sample Entropy (SampEn), capturing long-range temporal organization and local irregularity. Constrained conditions reduced GF coefficient of variation but increased both H and SampEn relative to spontaneous pre-trials, while LF indices remained largely unchanged. Thus, grip control under constraint became more temporally persistent and locally irregular, suggesting a more structured temporal organization rather than a simple loss of complexity. Intra-trial analyses further revealed an increase in H from the first to the second half of spontaneous trials, consistent with progressive self-organization in the absence of explicit constraints. Across conditions, H and SampEn were positively correlated for both GF and LF, suggesting that predictability and complexity are not necessarily inversely related in this context. Overall, these findings suggest that the human motor system adapts to force constraints not by suppressing variability, but by reorganizing it across scales, combining temporal persistence with local flexibility. This multidimensional characterization of variability may help refine theoretical models of optimal movement variability and may inform clinical or training approaches aimed at assessing or enhancing neuromotor adaptability.