Modeling Peripheral Muscle Fatigue Using a Variable Recovery Rate

Abstract

Muscle fatigue is a transient and reversible decrease in performance capacity after a period of physical exertion. A variety of approaches have been applied to model muscle fatigue. Recently a theoretical, phenomenal parameter-based model (Liu-Xia model) was proposed with the capability of predicting fatigue for tasks of any force-time history. The Liu-Xia model has two parameters F and R that define the fatigue and recovery behavior, respectively. Previously, F and R were treated as constant in model validation. In the current study, R is redefined as a function of exertion level in attempt to reflect the effect of muscle contraction on blood flow. The purpose is to examine if an R varying with exertion level can improve model prediction for low intensity, static and intermittent tasks. Particularly, R is modeled as a step-wise function of three regions: 0-10% maximum voluntary contraction (MVC), no occlusion; 10-50% MVC, 0-100% occlusion, assuming a linear relationship in the region; and 51-100%, full occlusion. The results suggest that an R varying with exertion level may serve as a viable way to improve model performance, dependent on a better modeling of the relationship between muscle contraction and blood flow.