A three-compartment muscle fatigue model accurately predicts joint-specific maximum endurance times for sustained isometric tasks.
A three-compartment muscle fatigue model accurately predicts joint-specific maximum endurance times for sustained isometric tasks.
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DOI：
10.1016/j.jbiomech.2012.04.018
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发表时间：
2012-06-26
影响因子：
2.4
通讯作者：
Heitsman J
中科院分区：
文献类型：
--
作者：
Frey-Law LA;Looft JM;Heitsman J
局部肌肉疲劳的发生发展通常用非线性强度-耐力时间曲线来描述.这些经验强度-ET关系已被充分记录，并在关节区域之间变化。我们以前提出了一个三室生物物理疲劳模型，由活动（MA），疲劳（MF）和休息（MR）肌肉的隔间（即状态），预测肌肉力量的衰减和恢复。本研究的目的是确定最佳模型参数值，疲劳（F）和恢复（R），其定义了肌肉状态之间的“流速”，并评估模型估计预期强度- ET曲线的准确性。使用网格搜索方法和改进的Monte Carlo模拟，超过100万个F和R排列被用来预测持续等距任务的最大ET，在9个强度范围从最大值的10 - 90%，增量为10%（超过900万个模拟总数）。最佳F和R值范围为0.00589（Fankle）和0.0182（Rankle）至0.00058（Fshoulder）和0.00168（Rshoulder），再现了具有低平均RMS误差的强度-ET曲线：肩部（2.7 s）、手/握（5.6 s）、膝（6.7 s）、躯干（9.3 s）、肘（9.9 s）和踝（11.2 s）。在不同的任务强度（15 - 95%，最大增量为10%）下测试模型产生的误差略高，但大部分在强度-ET曲线预期的95%预测区间内。我们的结论是，这种三室疲劳模型可以用来准确地表示关节特定的强度ET曲线，这可能是有用的人体工程学分析和/或数字人体建模应用。
The development of localized muscle fatigue has classically been described by the nonlinear intensity – endurance time (ET) curve. 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. 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.
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影响因子：
2.4
作者：
Frey Law LA;Avin KG
通讯作者：
Avin KG
DOI：
10.1016/s0169-8141(02)00078-1
发表时间：
2002-08-01
影响因子：
3.1
作者：
Garg, A;Hermann, KT;Kapellusch, JM
通讯作者：
Kapellusch, JM
影响因子：
3
作者：
Ng, Laurel J.;Sih, Bryant L.;Stuhmiller, James H.
通讯作者：
Stuhmiller, James H.
影响因子：
2.4
作者：
Xia, Ting;Law, Laura A. Frey
通讯作者：
Law, Laura A. Frey
影响因子：
2.4
作者：
HAGBERG, M
通讯作者：
HAGBERG, M