![]() The experimental relationships of muscle geometry in different postures are the high-dimensional spatial transformations that can be approximated by relatively simple functions, which opens the opportunity for machine learning (ML) applications. PeerJ Computer Science 7: e663 ĭeep learning is a relatively new computational technique for the description of the musculoskeletal dynamics. Solving musculoskeletal biomechanics with machine learning. ![]() Cite this article Smirnov Y, Smirnov D, Popov A, Yakovenko S. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. ![]() Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. 8 Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, West Virginia, United States DOI 10.7717/peerj-cs.663 Published Accepted Received Academic Editor Li Zhang Subject Areas Bioinformatics, Computational Biology, Human-Computer Interaction, Algorithms and Analysis of Algorithms, Artificial Intelligence Keywords Machine learning, Deep neural networks, Muscle, Hand, Real-time, Biomechanics Copyright © 2021 Smirnov et al. ![]()
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