Computational Models for Neuromuscular Function.
Computational Models for Neuromuscular Function.
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DOI：
10.1109/rbme.2009.2034981
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发表时间：
2009
影响因子：
17.6
通讯作者：
Theodorou EA
中科院分区：
文献类型：
--
作者：
Valero-Cuevas FJ;Hoffmann H;Kurse MU;Kutch JJ;Theodorou EA
神经肌肉系统的计算模型有可能使我们通过补充实验来更深入地了解神经肌肉功能和临床康复。作为一种提取和探索特定假设的手段，计算模型从先前的实验数据中产生，并激励未来的实验工作。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。我们的结论是，这些工具，结合使用时，有可能进一步我们的神经肌肉功能的理解，作为一个严格的手段来测试科学假设的方式，补充和利用实验数据。
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.