Rohan, Pierre-Yves, Fougeron, Nolwenn, Keenan, Bethany  ORCID: https://orcid.org/0000-0001-7787-2892, Pillet, Helene, Laporte, Sebastien, Osipov, Nikolay and Ryckelynck, David
2023.
Real-time numerical prediction of strain localization using dictionary-based ROM-nets for sitting-acquired deep tissue injury prevention.
Chinesta, Francisco, Cueto, Elias, Payan, Yohan and Ohayon, Jacques, eds.
Reduced Order Models for the Biomechanics of Living Organs,
Biomechanics of Living Organisms,
Elsevier,
pp. 385-402.
(10.1016/B978-0-32-389967-3.00027-5)
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Abstract
A dictionary-based ROM-net was developed based on the full-order models of 16 subjects (9 men and 7 women) for the assessment of subject-specific in-vivo subdermal soft tissue strains and stresses in the ischial regions during sitting. Because of the limited training data available, a data augmentation scheme was proposed combining submodeling a statistical shape model and the generation of synthetic data. A dictionary was defined as a collection of all the representative subjects identified using the K-medoid clustering of the training data set. Finally, a decision-tree classifier, acting as a model selector, was trained with a database of 707 labelled finite element models. The 202 validation data and the 6 unseen test data were used to evaluate the classification performance of the trained decision-tree classifier. The performance of the dictionary-based ROM was satisfactory (accuracy of 100% on the training data; accuracy of 50% on the unseen data). The computation of the strain field by the decision-tree classifier requires 0.1 s compared to 20 m for the submodel and about 8 h for the high-fidelity model. The projection error was 9%. As is the case with machine learning techniques, the prediction accuracy of the proposed approach is expected to improve significantly as the amount of training data increases.
| Item Type: | Book Section |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Engineering |
| Publisher: | Elsevier |
| ISBN: | 9780323899673 |
| Related URLs: | |
| Date of First Compliant Deposit: | 4 November 2022 |
| Date of Acceptance: | 25 September 2022 |
| Last Modified: | 09 Oct 2025 08:52 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/153984 |
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