Computational Models for Neuromuscular Function

Computational Models for Neuromuscular Function

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 p...

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Bibliographic Details
Published in:IEEE reviews in biomedical engineering Vol. 2; pp. 110 - 135
Main Authors: Valero-Cuevas, F.J., Hoffmann, H., Kurse, M.U., Kutch, J.J., Theodorou, E.A.
Format: Journal Article
Language:English
Published: United States IEEE 2009
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Anatomy
Biomechanics
Biomedical computing
computational methods
Computational modeling
Machine learning
modeling
Muscles
Musculoskeletal system
Neuromuscular
neuromuscular control
Physics computing
Predictive models
Testing
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Summary: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.
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All authors contributed equally to this work.
The authors are with The University of Southern California, Los Angeles, CA 90089-2905.
ISSN:1937-3333
1941-1189
DOI:10.1109/RBME.2009.2034981