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Healthy knee kinematic phenotypes identification based on a clustering data analysis [r-libre/2839]

Mezghani, Neila; Soltana, Rayan; Ouakrim, Youssef; Cagnin, Alix; Fuentes, Alexandre; Hagemeister, Nicola, & Venditolli, Pascal-André (2021). Healthy knee kinematic phenotypes identification based on a clustering data analysis. Applied Sciences, 11 (24), 12054. https://doi.org/10.3390/app112412054

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Content : Published Version
License : Creative Commons Attribution.
Item Type: Journal Articles
Refereed: Yes
Status: Published
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.
Official URL: https://www.mdpi.com/2076-3417/11/24/12054/htm
Depositor: Ayena, Johannes
Owner / Manager: Johannes Ayena
Deposited: 16 Jan 2023 19:47
Last Modified: 16 Jan 2023 19:47

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