Valero-Cuevas Francisco J, Hoffmann Heiko, Kurse Manish U, Kutch Jason J, Theodorou Evangelos A
IEEE Rev Biomed Eng. 2009;2:110-135. doi: 10.1109/RBME.2009.2034981.
Computational models of the neuromuscular system hold the potential to allow us to reach a deeper understanding of neuromuscular function and clinical rehabilitation by complementing experimentation. By serving as a means to distill and explore specific hypotheses, computational models emerge from prior experimental data and motivate future experimental work. Here we review computational tools used to understand neuromuscular function including musculoskeletal modeling, machine learning, control theory, and statistical model analysis. We conclude that these tools, when used in combination, have the potential to further our understanding of neuromuscular function by serving as a rigorous means to test scientific hypotheses in ways that complement and leverage experimental data.
神经肌肉系统的计算模型有潜力通过补充实验，让我们更深入地理解神经肌肉功能和临床康复。作为提炼和探索特定假设的一种手段，计算模型源于先前的实验数据，并推动未来的实验工作。在这里，我们回顾了用于理解神经肌肉功能的计算工具，包括肌肉骨骼建模、机器学习、控制理论和统计模型分析。我们得出结论，这些工具结合使用时，有可能通过作为一种严谨的手段来测试科学假设，以补充和利用实验数据的方式，进一步加深我们对神经肌肉功能的理解。
