Models, data and supplementary material for 'Comparative Validation of two Patient-Specific Modelling Pipelines for Predicting Knee Joint Forces During Level Walking'

Princelle, Domitille (2023) Models, data and supplementary material for 'Comparative Validation of two Patient-Specific Modelling Pipelines for Predicting Knee Joint Forces During Level Walking'. University of Bologna. DOI 10.6092/unibo/amsacta/7153. [Dataset]
Full text disponibile come:
[thumbnail of README] Documento di testo(testo) (README)
Licenza: Creative Commons: Attribuzione 4.0(CC BY 4.0)

Download (3kB)
[thumbnail of Models, data and supplementary material] Archivio (Models, data and supplementary material)
Licenza: Creative Commons: Attribuzione 4.0(CC BY 4.0)

Download (127MB)

Abstract

This dataset contains models, data and results, and supplementary figures and material related to the article "Comparative Validation of two Patient-Specific Modelling Pipelines for Predicting Knee Joint Forces During Level Walking". This dataset is built from the last four editions of the open source dataset of the "Grand Challenge Competition to Predict In Vivo Knee Loads" (Fregly et al., 2012), which is publicly available on the SimTK platform (https://simtk.org/projects/kneeloads).

Abstract
Tipologia del documento
Dataset
Autori
AutoreAffiliazioneORCID
Princelle, DomitilleAlma Mater Studiorum - University of Bologna; IRCCS Istituto Ortopedico Rizzoli0000-0002-1215-9268
Parole chiave
Musculokseletal model, Subject-specific model, image-based model, joint load, predictive accuracy
Settori scientifico-disciplinari
DOI
Contributors
Contributor
Affiliazione
ORCID
Tipo
Princelle, Domitille
Alma Mater Studiorum - University of Bologna; IRCCS Istituto Ortopedico Rizzoli
Contact person
Davico, Giorgio
Alma Mater Studiorum - University of Bologna; IRCCS Istituto Ortopedico Rizzoli
Researcher
Viceconti, Marco
Alma Mater Studiorum - University of Bologna; IRCCS Istituto Ortopedico Rizzoli
Supervisor
Data di deposito
02 Feb 2023 08:37
Ultima modifica
02 Set 2023 21:00
Nome del Progetto
ISW - In Silico World: Lowering barriers to ubiquitous adoption of In Silico Trials
Programma di finanziamento
EC - H2020
URI

Altri metadati

Statistica sui download

Statistica sui download

Gestione del documento: Visualizza il documento

^