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Development of a statistical model of knee kinetics for applications in pre-clinical testing

Record type: Article

Pre-clinical computational testing of total knee replacements (TKRs) often only considers a single patient model with simplified applied loads. In studies of multiple patients, most only take into account geometric differences, especially in studies on the knee. Limited availability of kinetic data means that it is difficult to account for inter-patient variability. Principal component analysis (PCA) based statistical models have been used to capture the variation of a set of data and generate new instances of the data. This study presents a method to create a statistical model of kinetic waveform data. A PCA based statistical model was created of the tibiofemoral joint loads for level gait of preoperative TKR patients using data predicted from a musculoskeletal model. A reconstruction test showed that, using principal components (PCs) representing 95% variance, the median root-mean-squared (RMS) error was <0.1 body weight (BW) for the forces and <0.001 BWm for the moments. Leave-one-out tests were also performed and although the median RMS error increased for each load in comparison to the reconstruction error (maximum was 0.2 BW for the axial force and 0.012 BWm for the varus–valgus moment) these were considered within an acceptable limit. The purpose of creating a statistical model is to be able to sample a large set of data representing a population from a small set of clinical data. Such models can potentially be used in population based studies of TKRs incorporating inter-patient variability.

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Citation

Galloway, Francis, Worsley, Peter, Stokes, Maria, Nair, Prasanth and Taylor, Mark (2012) Development of a statistical model of knee kinetics for applications in pre-clinical testing Journal of Biomechanics, 45, (1), pp. 191-195. (doi:10.1016/j.jbiomech.2011.09.009). (PMID:22030123).

More information

e-pub ahead of print date: 24 October 2011
Published date: 3 January 2012
Keywords: knee kinetics, statistical model, level gait
Organisations: Bioengineering Group

Identifiers

Local EPrints ID: 201277
URI: http://eprints.soton.ac.uk/id/eprint/201277
ISSN: 0021-9290
PURE UUID: b01c5590-74c5-4bc9-969a-b54e778ead74
ORCID for Maria Stokes: ORCID iD orcid.org/0000-0002-4204-0890

Catalogue record

Date deposited: 28 Oct 2011 12:46
Last modified: 18 Jul 2017 11:13

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Contributors

Author: Francis Galloway
Author: Peter Worsley
Author: Maria Stokes ORCID iD
Author: Prasanth Nair
Author: Mark Taylor

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