Publication result detail

Advanced sampling discovers apparently similar ankle models with distinct internal load states under minimal parameter modification

VOŘECHOVSKÝ, M.; CISZKIEWICZ, A.

Original Title

Advanced sampling discovers apparently similar ankle models with distinct internal load states under minimal parameter modification

English Title

Advanced sampling discovers apparently similar ankle models with distinct internal load states under minimal parameter modification

Type

WoS Article

Original Abstract

Creating valid and trustworthy models is a key issue in biomedical engineering that affects the quality of life of both patients and healthy individuals in various scientific and industrial domains. This however is a difficult task due to the complex nature of biomechanical joints. In this study, a sampling strategy combining Genetic Algorithm and clustering is proposed to investigate biomechanical joints. A computational model of a human ankle joint with 43 input parameters serves as an illustrative case for the procedure. The Genetic Algorithm is used to efficiently search for distinct variants of the model with similar output, while clustering helps to quantify the obtained results. The search is performed in a close vicinity to the original model, mimicking subjective decisions in parameter acquisition. The method reveals twelve distinct clusters in the model parameter set, all resulting in the same angular displacements. These clusters correspond to three unique internal load states for the model, confirming the complex nature of the ankle. The proposed approach is general and could be applied to study other models in mechanical engineering and robotics.

English abstract

Creating valid and trustworthy models is a key issue in biomedical engineering that affects the quality of life of both patients and healthy individuals in various scientific and industrial domains. This however is a difficult task due to the complex nature of biomechanical joints. In this study, a sampling strategy combining Genetic Algorithm and clustering is proposed to investigate biomechanical joints. A computational model of a human ankle joint with 43 input parameters serves as an illustrative case for the procedure. The Genetic Algorithm is used to efficiently search for distinct variants of the model with similar output, while clustering helps to quantify the obtained results. The search is performed in a close vicinity to the original model, mimicking subjective decisions in parameter acquisition. The method reveals twelve distinct clusters in the model parameter set, all resulting in the same angular displacements. These clusters correspond to three unique internal load states for the model, confirming the complex nature of the ankle. The proposed approach is general and could be applied to study other models in mechanical engineering and robotics.

Keywords

Genetic algorithm; Clustering; Uncertainty quantification; Multibody system method

Key words in English

Genetic algorithm; Clustering; Uncertainty quantification; Multibody system method

Authors

VOŘECHOVSKÝ, M.; CISZKIEWICZ, A.

RIV year

2025

Released

25.04.2024

Publisher

ELSEVIER

Location

AMSTERDAM

ISBN

1877-7503

Periodical

Journal of Computational Science

Volume

82

Number

102425

State

Kingdom of the Netherlands

Pages count

10

URL

https://doi.org/10.1016/j.jocs.2024.102425

BibTex

@article{BUT193732,
  author="Miroslav {Vořechovský} and Adam {CISZKIEWICZ}",
  title="Advanced sampling discovers apparently similar ankle models with distinct internal load states under minimal parameter modification",
  journal="Journal of Computational Science",
  year="2024",
  volume="82",
  number="102425",
  pages="10",
  doi="10.1016/j.jocs.2024.102425",
  issn="1877-7503",
  url="https://doi.org/10.1016/j.jocs.2024.102425"
}