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Publication

Machine learning methods to support personalized neuromusculoskeletal modelling.

Journal Contribution - e-publication

Many biomedical, orthopaedic, and industrial applications are emerging that will benefit from personalized neuromusculoskeletal models. Applications include refined diagnostics, prediction of treatment trajectories for neuromusculoskeletal diseases, in silico design, development, and testing of medical implants, and human-machine interfaces to support assistive technologies. This review proposes how physics-based simulation, combined with machine learning approaches from big data, can be used to develop high-fidelity personalized representations of the human neuromusculoskeletal system. The core neuromusculoskeletal model features requiring personalization are identified, and big data/machine learning approaches for implementation are presented together with recommendations for further research.
Journal: Biomechanics and Modeling in Mechanobiology
ISSN: 1617-7959
Issue: 4
Volume: 19
Pages: 1169 - 1185
Publication year:2020
BOF-keylabel:yes
IOF-keylabel:yes
BOF-publication weight:1
CSS-citation score:2
Authors:International
Authors from:Higher Education
Accessibility:Open