A three-compartment controller model (3CC) predicts muscle fatigue development. Determination of fatigue (F) and recovery (R) model parameters is critical for model accuracy. Numerical methods can be used to determine parameter values using maximum voluntary contractions (MVCs) as input. We tested the effects of using reduced MVC data on parameter solutions using twenty published datasets of intermittent, isometric contractions. The work here examines three sampling variations using approximately half of the MVCs: MVC measurements distributed equally (dMVC), split between the initial and final times (sMVC), and only during the first half (fMVC). Furthermore, solved F and R parameters were used to model fatigue development for three hypothetical task scenarios. Both model parameters and predictions were statistically insensitive to measured data reduction using dMVC, followed closely by sMVC. However, using the fMVC reduction frequently resulted in overestimated parameter values and produced significantly larger prediction errors. We conclude that parameter solutions are robust when using fewer MVCs as long as they are sampled in a manner that captures later fatigue behavior.

三室控制器模型（3CC）可以预测肌肉疲劳的发展。确定疲劳（F）和恢复（R）模型参数对于模型准确性至关重要。使用最大自愿收缩量（MVC）作为输入，可以使用数值方法来确定参数值。我们使用二十个已发布的间歇性等距收缩数据集，测试了在参数解上使用减少的MVC数据的效果。此处的工作使用大约一半的MVC检验了三个采样变化：MVC度量平均分布（dMVC），在初始时间和最终时间之间划分（sMVC），并且仅在前一半（fMVC）期间。此外，已解决的F和R参数被用于为三个假设任务场景建模疲劳发展。模型参数和预测对使用dMVC（紧随其后的是sMVC）的测量数据减少量在统计上都不敏感。但是，使用fMVC减少通常会导致参数值被高估，并产生明显更大的预测误差。我们得出的结论是，使用较少的MVC时，只要以捕获后期疲劳行为的方式对它们进行采样，参数解决方案就很健壮。