Machine learning methods to support personalized neuromusculoskeletal modelling
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Killen, Bryce Adrian
Pizzolato, C
Carty, CP
Diamond, LE
Modenese, L
Fernandez, J
Davico, G
Barzan, M
Lenton, G
da Luz, S Brito
Suwarganda, E
Devaprakash, D
Korhonen, RK
Alderson, JA
Besier, TF
Barrett, RS
Lloyd, DG
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Abstract
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.
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Biomechanics and Modeling in Mechanobiology
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19
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LP150100905
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Subject
Machine learning
Artificial intelligence
Biomechanical engineering
Human biophysics
Biomedical engineering
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Citation
Saxby, DJ; Killen, BA; Pizzolato, C; Carty, CP; Diamond, LE; Modenese, L; Fernandez, J; Davico, G; Barzan, M; Lenton, G; da Luz, SB; Suwarganda, E; Devaprakash, D; Korhonen, RK; Alderson, JA; Besier, TF; Barrett, RS; Lloyd, DG, Machine learning methods to support personalized neuromusculoskeletal modelling, Biomechanics and Modeling in Mechanobiology, 2020, 19, pp.1169-1185