A novel approach for determining fatigue resistances of different muscle groups in static cases

Abstract

In ergonomics and biomechanics, muscle fatigue models based on maximum endurance time (MET) models are often used to integrate fatigue effect into ergonomic and biomechanical application. However, due to the empirical principle of those MET models, the disadvantages of this method are: 1) the MET models cannot reveal the muscle physiology background very well; 2) there is no general formation for those MET models to predict MET. In this paper, a theoretical MET model is extended from a simple muscle fatigue model with consideration of the external load and maximum voluntary contraction in passive static exertion cases. The universal availability of the extended MET model is analyzed in comparison to 24 existing empirical MET models. Using mathematical regression method, 21 of the 24 MET models have intraclass correlations over 0.9, which means the extended MET model could replace the existing MET models in a general and computationally efficient way. In addition, an important parameter, fatigability (or fatigue resistance) of different muscle groups, could be calculated via the mathematical regression approach. Its mean value and its standard deviation are useful for predicting MET values of a given population during static operations. The possible reasons influencing the fatigue resistance were classified and discussed, and it is still a very challenging work to find out the quantitative relationship between the fatigue resistance and the influencing factors. Relevance to industry :MSD risks can be reduced by correct evaluation of static muscular work. Different muscle groups have different properties, and a generalized MET model is useful to simplify the fatigue analysis and fatigue modeling, especially for digital human techniques and virtual human simulation tools.

Force time-history affects fatigue accumulation during repetitive handgrip tasks

“…Ma et al (2011) force level was set to an exponent to predict endurance times, to define fatigue. They also included different constants for each joint.…”

A three-compartment muscle fatigue model accurately predicts joint-specific maximum endurance times for sustained isometric tasks

“…In the past 50+ years, several authors have proposed updated versions of this classic nonlinear curve (Monod and Scherrer, 1965; Hagberg, 1981; Huijgens, 1981; Rose et al, 2000; Garg et al, 2002). More recently, comparisons between several of these models observed that equations varied by joint region (El Ahrache et al, 2006; Ma et al, 2009; Ma et al, 2011). Likewise, a large meta-analysis of 194 publications involving experimental fatigue data confirmed joint-specific intensity-ET relationships (Frey Law and Avin, 2010).…”

Muscle Fatigue Analysis Using OpenSim

“…For example, females are found to be more fatigue-resistant than males [15]; and the older groups shows significantly much less force loss than the younger group after a certain exercises [16]. Fatigability k has been determined by comparing the Force-Load muscle fatigue model with the empirical maximal endurance models [12]. The determined value of k varies from 0.87 min −1 to 2.15 min −1 for general muscle groups.…”