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

Abstract

The development of localized muscle fatigue has classically been described by the nonlinear intensity – endurance time (ET) curve (Rohmert, 1960; El Ahrache et al., 2006). These empirical intensity-ET relationships have been well-documented and vary between joint regions. We previously proposed a three-compartment biophysical fatigue model, consisting of compartments (i.e. states) for active (MA), fatigued (MF), and resting (MR) muscle, to predict the decay and recovery of muscle force (Xia and Frey Law, 2008). The purpose of this investigation was to determine optimal model parameter values, fatigue (F) and recovery (R), which define the “flow rate” between muscle states and to evaluate the model’s accuracy for estimating expected intensity – ET curves. Using a grid-search approach with modified Monte Carlo simulations, over 1 million F and R permutations were used to predict the maximum ET for sustained isometric tasks at 9 intensities ranging from 10 – 90% of maximum in 10% increments (over 9 million simulations total). Optimal F and R values ranged from 0.00589 (Fankle) and 0.0182 (Rankle) to 0.00058 (Fshoulder) and 0.00168 (Rshoulder), reproducing the intensity-ET curves with low mean RMS errors: shoulder (2.7s), hand/grip (5.6s), knee (6.7s), trunk (9.3s), elbow (9.9s), and ankle (11.2s). Testing the model at different task intensities (15 – 95% maximum in 10% increments) produced slightly higher errors, but largely within the 95% prediction intervals expected for the intensity-ET curves. We conclude that this three-compartment fatigue model can be used to accurately represent joint-specific intensity-ET curves, which may be useful for ergonomic analyses and/or digital human modeling applications.

Accuracy of the surface electromyography RMS processing for the diagnosis of myogenous temporomandibular disorder

“…In this context, the assessment of the accuracy of a diagnostic test must be considered in the determination of the validity of the sEMG (Leeflang et al, 2008). For such, it is important to establish a cutoff point value that allows the interpretation of the results of the test based on a dichotomy (presence or absence of the condition in question) (Akobeng, 2007;Frey-Law et al, 2012).…”

Adaptations to isolated shoulder fatigue during simulated repetitive work. Part II: Recovery

“…Both muscle fatigue and recovery are time-dependent processes but each proceeds at a different rate (Lucidi and Lehman, 1992;Vollestad and Sejersted, 1988), with recovery statistically modeled to occur 10-15 times slower than the fatiguing process itself (Frey-Law et al, 2012). Long recovery times for fatigued muscles is especially relevant in the shoulder given the frequent demands placed on the postural and stabilizing muscles of the shoulder complex, specifically, the rotator cuff muscles (Karduna et al, 1996;Labriola et al, 2004).…”

Movement Variability and Digital Human Models: Development of a Demonstrator Taking the Effects of Muscular Fatigue into Account

“…For example, Ding et al [14] use a biochemical approach, Böl et al [15] apply a "finite elements" approach, and a biophysical approach is developed by Liu et al [16] in their three-compartment model. Of all these approaches, the three-compartment model appears the best adapted to integration into DHM-type tools, and the literature presents several studies of this type of model with virtual humans in specific conditions (static effort, estimation of MET) [12,17,18].…”

“…For this work, we retain the model of fatigue proposed by Frey-Law et al [17]. This model takes phenomena related to fatigue and recovery into account by considering the whole force-generation chain, from the CNS to the various muscle fibres.…”

“…Parameter C is bidirectional and non-linear, depending on the expected effort (Target Load -TL) and on the current level of fatigue. For a comprehensive description of this model, see [17].…”