Research Data Leeds Repository

Data associated with ‘Development of a specimen-specific in vitro pre-clinical simulation model of the human cadaveric knee with appropriate soft tissue constraints’

Liu, Aiqin and Sanderson, William J and Ingham, Eileen and Fisher, John and Jennings, Louise M (2020) Data associated with ‘Development of a specimen-specific in vitro pre-clinical simulation model of the human cadaveric knee with appropriate soft tissue constraints’. University of Leeds. [Dataset] https://doi.org/10.5518/772

This item is part of the The Institute of Medical and Biological Engineering Knee Dataset collection.

Dataset description

The dataset will include all the data for figures in the manuscripts as shown in the followings: 1) Kinematic input profiles for the human cadaveric knee. 2) Average AP displacement and TR angle of the 4 human knee specimens in a human walking gait cycle (both AP and TR force driven condition). 3) Typical kinematic profiles from one donor under different spring conditions for different spring rates and free lengths/degrees 4) Comparison of kinematic profiles (AP displacement and TR angle) of the intact knee and the most appropriate spring constraints from four human knee specimens

Subjects: H000 - Engineering > H100 - General engineering > H160 - Bioengineering, biomedical engineering & clinical engineering > H162 - Biomechanics (including fluid & solid mechanics)
Divisions: Faculty of Biological Sciences > Institute of Medical and Biological Engineering
Faculty of Engineering and Physical Sciences > Institute of Medical and Biological Engineering
Faculty of Engineering and Physical Sciences > School of Mechanical Engineering > Institute of Medical and Biological Engineering
Related resources:
LocationType
http://eprints.whiterose.ac.uk/166382/Publication
https://doi.org/10.1371/journal.pone.0238785Publication
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited: 28 Aug 2020 14:10
URI: http://archive.researchdata.leeds.ac.uk/id/eprint/736

Files

Data

Research Data Leeds Repository is powered by EPrints
Copyright © 2024 University of Leeds