一种确定静态情况下不同肌群疲劳抗力的新方法
A novel approach for determining fatigue resistances of different muscle groups in static cases
摘要：
在人机工程学和生物力学中，基于最大耐受时间（MET）模型的肌肉疲劳模型常被用来将疲劳效应整合到人机工程学和生物力学应用中。然而，由于MET模型的经验性原则，该方法的缺点是：1）MET模型不能很好地揭示肌肉生理背景; 2）MET模型预测MET没有通用的形式。本文从简单的肌肉疲劳模型出发，考虑了被动静态用力情况下的外部负荷和最大随意收缩，建立了一个理论MET模型。的扩展MET模型的普遍可用性进行了分析比较，现有的24个经验MET模型。采用数学回归方法，24个MET模型中有21个模型的组内相关系数超过0.9，这意味着扩展的MET模型可以以一种通用且计算效率高的方式取代现有的MET模型。此外，一个重要的参数，不同肌肉群的疲劳性（或抗疲劳性），可以通过数学回归方法计算。它的平均值和标准差可用于预测静态操作期间给定总体的MET值。对影响疲劳抗力的可能原因进行了分类和讨论，找出疲劳抗力与影响因素之间的定量关系仍然是一项极具挑战性的工作。
英文摘要：
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.
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