Rashedi Ehsan, Nussbaum Maury A
Department of Industrial and System Engineering, Virginia Tech, Blacksburg, Virginia, United States of America.
Department of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, Virginia, United States of America.
PLoS One. 2015 Dec 14;10(12):e0143872. doi: 10.1371/journal.pone.0143872. eCollection 2015.
Muscle fatigue models (MFM) have broad potential application if they can accurately predict muscle capacity and/or endurance time during the execution of diverse tasks. As an initial step toward facilitating improved MFMs, we assessed the sensitivity of selected existing models to their inherent parameters, specifically that model the fatigue and recovery processes, and the accuracy of model predictions. These evaluations were completed for both prolonged and intermittent isometric contractions, and were based on model predictions of endurance times. Based on a recent review of the literature, four MFMs were initially chosen, from which a preliminary assessment led to two of these being considered for more comprehensive evaluation. Both models had a higher sensitivity to their fatigue parameter. Predictions of both models were also more sensitive to the alteration of their parameters in conditions involving lower to moderate levels of effort, though such conditions may be of most practical, contemporary interest or relevance. Although both models yielded accurate predictions of endurance times during prolonged contractions, their predictive ability was inferior for more complex (intermittent) conditions. When optimizing model parameters for different loading conditions, the recovery parameter showed considerably larger variability, which might be related to the inability of these MFMs in simulating the recovery process under different loading conditions. It is argued that such models may benefit in future work from improving their representation of recovery process, particularly how this process differs across loading conditions.
肌肉疲劳模型(MFM)如果能够准确预测在执行各种任务期间的肌肉能力和/或耐力时间,就具有广泛的潜在应用价值。作为迈向改进肌肉疲劳模型的第一步,我们评估了所选现有模型对其固有参数的敏感性,特别是那些对疲劳和恢复过程进行建模的参数,以及模型预测的准确性。这些评估针对长时间和间歇性等长收缩完成,并基于耐力时间的模型预测。基于最近的文献综述,最初选择了四个肌肉疲劳模型,通过初步评估,其中两个被考虑进行更全面的评估。两个模型对其疲劳参数都具有更高的敏感性。在涉及低至中等努力程度的条件下,两个模型的预测对其参数的变化也更敏感,尽管这些条件可能是当前最具实际意义或相关性的。虽然两个模型在长时间收缩期间对耐力时间都做出了准确的预测,但在更复杂(间歇性)的条件下,它们的预测能力较差。在针对不同负荷条件优化模型参数时,恢复参数显示出相当大的变异性,这可能与这些肌肉疲劳模型无法模拟不同负荷条件下的恢复过程有关。有人认为,在未来的工作中,此类模型可能会从改进其对恢复过程的描述中受益,特别是恢复过程在不同负荷条件下的差异。