The gait1415+2 OpenSim musculoskeletal model of transfemoral amputees with a generic bone-anchored prosthesis.

Musculoskeletal model for artificial intelligence OpenSim Osseointegrated transfemoral amputees

Journal

Medical engineering & physics
ISSN: 1873-4030
Titre abrégé: Med Eng Phys
Pays: England
ID NLM: 9422753

Informations de publication

Date de publication:
Jan 2024
Historique:
received: 02 09 2023
revised: 13 11 2023
accepted: 16 12 2023
medline: 17 2 2024
pubmed: 17 2 2024
entrez: 16 2 2024
Statut: ppublish

Résumé

This short communication presents the gait1415+2 musculoskeletal model, that has been developed in OpenSim to describe the lower-extremity of a human subject with transfemoral amputation wearing a generic lower-limb bone-anchored prosthesis. The model has fourteen degrees of freedom, governed by fifteen musculotendon units (placed at the contralateral and residual limbs) and two generic actuators (one placed at the knee joint and one at the ankle joint of the prosthetic leg). Even though the model is a simplified abstraction, it is capable of generating a human-like walking gait and, specifically, it is capable of reproducing both the kinematics and the dynamics of a person with transfemoral amputation wearing a bone-anchored prosthesis during normal level-ground walking. The model is released as support material to this short communication with the final goal of providing the scientific community with a tool for performing forward and inverse dynamics simulations, and for developing computationally-demanding control schemes based on artificial intelligence methods for lower-limb prostheses.

Identifiants

pubmed: 38365342
pii: S1350-4533(23)00146-7
doi: 10.1016/j.medengphy.2023.104091
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

104091

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest No conflict of interest.

Auteurs

Raffaella Carloni (R)

Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, Nijenborgh 9, Groningen, 9747 AG, the Netherlands. Electronic address: r.carloni@rug.nl.

Rutger Luinge (R)

Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, Nijenborgh 9, Groningen, 9747 AG, the Netherlands.

Vishal Raveendranathan (V)

Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, Nijenborgh 9, Groningen, 9747 AG, the Netherlands.

Classifications MeSH