FRANÇAIS
A showcase of ÉTS researchers’ publications and other contributions
SEARCH

Healthy knee kinematic phenotypes identification based on a clustering data analysis

Downloads

Downloads per month over past year

Mezghani, Neila, Soltana, Rayan, Ouakrim, Youssef, Cagnin, Alix, Fuentes, Alexandre, Hagemeister, Nicola et Vendittoli, Pascal- André. 2021. « Healthy knee kinematic phenotypes identification based on a clustering data analysis ». Applied Sciences, vol. 11, nº 24.

[thumbnail of Hagemeister-N-2021-23841.pdf]
Preview
PDF
Hagemeister-N-2021-23841.pdf - Published Version
Use licence: Creative Commons CC BY.

Download (19MB) | Preview

Abstract

The purpose of this study is to identify healthy phenotypes in knee kinematics based on clustering data analysis. Our analysis uses the 3D knee kinematics curves, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation, measured via a KneeKG™ system during a gait task. We investigated two data representation approaches that are based on the joint analysis of the three dimensions. The first is a global approach that is considered a concatenation of the kinematic data without any dimensionality reduction. The second is a local approach that is considered a set of 69 biomechanical parameters of interest extracted from the 3D kinematic curves. The data representations are followed by a clustering process, based on the BIRCH (balanced iterative reducing and clustering using hierarchies) discriminant model, to separate 3D knee kinematics into homogeneous groups or clusters. Phenotypes were obtained by averaging those groups. We validated the clusters using inter-cluster correlation and statistical hypothesis tests. The simulation results showed that the global approach is more efficient, and it allows the identification of three descriptive 3D kinematic phenotypes within a healthy knee population.

Item Type: Peer reviewed article published in a journal
Professor:
Professor
Hagemeister, Nicola
Affiliation: Génie des systèmes
Date Deposited: 24 Jan 2022 17:19
Last Modified: 03 Mar 2022 15:39
URI: https://espace2.etsmtl.ca/id/eprint/23841

Actions (login required)

View Item View Item